UXFest and My Talk on Second Screen Experiences

October 11th, 2013 by Chris Allen

Chris UX Fest

Last week I presented at a really well attended and inspiring conference here in Boston called UXFest run by design firm Fresh Tilled Soil. I spoke to a packed room of UX enthusiasts that were interested in what I had to say about the direction of user experiences in video games and how these designs can play out in other industries. I got quite a few requests to post my slides from the talk, but given that I tend to take the approach of one image per slide with little-to-no text, that simply wasn’t going to work. For example a slide that shows nothing but an iPhone followed by a photo of a remote controlled helicopter wouldn’t make much sense without some context. So with that, here’s a summary of my talk in blog form. Read the rest of this entry »

My Thoughts on Glass

August 27th, 2013 by Chris Allen

I recently took a trip down to NYC a few weeks ago to pick up my new Google Glass. I figured now that we’ve had a bit of time to play with it here at Infrared5 and Brass Monkey, it’s time to write a post on our findings and what we think.

Read the rest of this entry »

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Infrared5 Ultimate Coder Week Six: GDC, Mouth Detection Setbacks, Foot Tracking and Optimizations Galore

April 3rd, 2013 by Chris Allen

For week six Chris and Aaron made the trek out to San Francisco to the annual Game Developers Conference (GDC) where they showed the latest version of our game Kiwi Catapult Revenge. The feedback we got was amazing! People were blown away at the head tracking performance that we’ve achieved, and everyone absolutely loved our unique art style. While the controls were a little difficult for some, that allowed us to gain some much needed insight into how to best fine tune the face tracking and the smartphone accelerometer inputs to make a truly killer experience. There’s nothing like live playtesting on your product!

Not only did we get a chance for the GDC audience to experience our game, we also got to meet some of the judges and the other Ultimate Coder competitors. There was an incredible amount of mutual respect and collaboration among the teams. The ideas were flowing on how to help improve each and every project in the competition. Chris gave some tips on video streaming protocols to Lee so that he will be able to stream over the internet with some decent quality (using compressed JPEGs would have only wasted valuable time). The guys from Sixense looked into Brass Monkey and how they can leverage that in their future games, and we gave some feedback to the Code Monkeys on how to knock out the background using the depth camera to prevent extra noise that messes with the controls they are implementing. Yes, this is a competition, but the overall feeling was one of wanting to see every team produce their very best.

The judges also had their fair share of positive feedback and enthusiasm. The quality of the projects obviously had impressed them, to the point that Nicole was quoted saying “I don’t know how we are going to decide”. We certainly don’t envy their difficult choice, but we don’t plan on making it any easier for them either. All the teams are taking it further and want to add even more amazing features to their applications before the April 12th deadline.

The staff in the Intel booth were super accommodating, and the exposure we got by being there was invaluable to our business. This is a perfect example of a win-win situation. Intel is getting some incredible demos of their new technology, and the teams are getting exposure and credibility by being in a top technology company’s booth. Not only that, but developers now get to see this technology in action, and can more easily discover more ways to leverage the code and techniques we’ve pioneered. Thank you Intel for being innovative and taking a chance on doing these very unique and experimental contests!

While Aaron and Chris were having a great time at GDC the rest of the team was cranking away. Steff ran into some walls with mouth detection for the breathing fire controls, but John, Rebecca and Elena were able to add more polish to the characters, environment and game play.

John added on a really compelling new feature – playing the game with your feet! We switched the detection algorithm so that it tracks your feet instead of your face. We call it Foot Tracking. It works surprisingly well, and the controls are way easier this way.

Steff worked on optimizing the face tracking algorithms and came up with some interesting techniques to get the job done.

This week’s tech tip and code snippet came to us during integration. We were working hard to combine the head tracking with the Unity game on the Ultrabook, and ZANG we had it working! But, there was a problem. It was slow. It was so slow it was almost unplayable. It was so slow that it definitely wasn’t “fun.” We had about 5 hours until Chris was supposed to go to the airport and we knew that the head tracking algorithms and the camera stream were slowing us down. Did we panic? (Don’t Panic!) No. And you shouldn’t either when faced with any input that is crushing the performance of your application. We simply found a clever way to lower the sampling rate but still have smooth output between frames.

The first step was to reduce the number of times we do a head tracking calculation per second. Our initial (optimistic) attempts were to update in realtime on every frame in Unity. Some computers could handle it, but most could not. Our Lenovo Yoga really bogged down with this. So, we introduced a framesToSkip constant and started sampling on every other frame. Then we hit a smoothing wall. Since the head controls affect every single pixel in the game window (by changing the camera projection matrix based on the head position), we needed to be smoothing the head position on every frame regardless of how often we updated the position from the camera data. Our solution was to sample the data at whatever frame rate we needed to preserve performance, save the head position at that instant as a target, and ease the current position to the new position on every single frame. That way, your sampling rate is down, but you’re still smoothing on every frame and the user feels like the game is reacting to their every movement in a non-jarring way. (For those wondering what smoothing algorithm we selected: Exponential Smoothing handles any bumps in the data between frames.) Code is below.

Feeling good about the result, we went after mouth open/closed detection with a vengeance! We thought we could deviate from our original plan of using AAM and POSIT, and lock onto the mouth using a mouth specific Haarcascade on the region of interest containing the face. The mouth Haarcascade does a great job finding and locking onto the mouth if the user is smiling – which is not so good for our purposes. We are still battling with getting a good lock on the mouth using a method that combines depth data with RGB, but we have seen why AAM exists for feature tracking. It’s not just something you can cobble together and have confidence that it will work well enough to act as an input for game controls.

Overall, this week was a step forward even with part of the team away. We’ve got some interesting and fun new features that we want to add as well. We will be sure to save that surprise for next week. Until then, please let us know if you have any questions and/or comments. May the best team win!

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Ultimate Coder Week #5: For Those About to Integrate We Salute You

March 21st, 2013 by Chris Allen

This post was featured on Intel Software’s blog, in conjunction with Intel’s Ultimate Coder Challenge. Keep checking back to read our latest updates!

We definitely lost one of our nine lives this week with integrating face tracking into our game, but we still have our cat’s eyes, and are still feel very confident that we will be able to show a stellar game at GDC. On the face tracking end of things we had some big wins. We are finally happy with the speed of the algorithms, and the way things are being tracked will work perfectly for putting into Kiwi Catapult Revenge. We completed some complex math to create very realistic perspective shifting in Unity. Read below on those details, as well as for some C# code to get it working yourself. As we just mentioned, getting a DLL that properly calls update() from Unity and passes in the tracking values isn’t quite there yet. We did get some initial integration with head tracking coming into Unity, but full integration with our game is going to have to wait for this week.

On the C++ side of things, we have successfully found the 3D position of the a face in the tracking space. This is huge! By tracking space, we mean the actual (x,y,z) position of the face from the camera in meters. Why do we want the 3D position of the face in tracking space? The reason is so that we can determine the perspective projection of the 3D scene (in game) from the player’s location. Two things made this task interesting: 1) The aligned depth data for a given (x,y) from the RGB image is full of holes and 2) the camera specs only include the diagonal field of view (FOV) and no sensor dimensions.

We got around the holes in the aligned depth data by first checking for a usable value at the exact (x, y) location, and if the depth value was not valid (0 or the upper positive limit), we would walk through the pixels in a rectangle of increasing size until we encountered a usable value. It’s not that difficult to implement, but annoying when you have the weight of other tasks on your back. Another way to put it: It’s a Long Way to the Top on this project.

The z-depth of the face comes back in millimeters right from the depth data, the next trick was to convert the (x, y) position from pixels on the RGB frame to meters in the tracking space. There is a great illustration here of how to break the view pyramid up to derive formulas for x and y in the tracking space. The end result is:
TrackingSpaceX = TrackingSpaceZ * tan(horizontalFOV / 2) * 2 * (RGBSpaceX – RGBWidth / 2) / RGBWidth)
TrackingSpaceY = TrackingSpaceZ * tan(verticalFOV / 2) * 2 * (RGBSpaceY – RGBHeight / 2) / RGBHeight)

Where TrackingSpaceZ is the lookup from the depth data, horizontalFOV, and verticalFOV are are derived from the diagonal FOV in the Creative Gesture Camera Specs (here). Now we have the face position in tracking space! We verified the results using a nice metric tape measure (also difficult to find at the local hardware store – get with the metric program, USA!)

From here, we can determine the perspective projection so the player will feel like they are looking through a window into our game. Our first pass at this effect involved just changing the rotation and position of the 3D camera in our Unity scene, but it just didn’t look realistic. We were leaving out adjustment of the projection matrix to compensate for the off-center view of the display. For example: consider two equally-sized (in screen pixels) objects at either side of the screen. When the viewer is positioned nearer to one side of the screen, the object at the closer edge appears larger to the viewer than the one at the far edge, and the display outline becomes trapezoidal. To compensate, the projection should be transformed with a shear to maintain the apparent size of the two objects; just like looking out a window! To change up our methods and achieve this effect, we went straight to the ultimate paper on the subject: Robert Koomla’s Generalized Perspective Projection. Our port of his algorithm into C#/Unity is below.

The code follows the mouse pointer to change perspective (not a tracked face) and does not change depth (the way a face would). We are currently in the midst of wrapping our C++ libs into a DLL for Unity to consume and enable us to grab the 3D position of the face and then compute the camera projection matrix using the face position and the position of the computer screen in relation to the camera.

Last but not least we leave you with this week’s demo of the game. Some final art for UI elements are in, levels of increasing difficulty have been implemented and some initial sound effects are in the game.

As always, please ask if you have any questions on what we are doing, or if you just have something to say we would love to hear from you. Leave us a comment! In the meantime we will be coding All Night Long!

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Infrared5 Ultimate Coder Update 4: Flamethrowers, Wingsuits, Rearview Mirrors and Face Tracking!

March 18th, 2013 by admin

This post was featured on Intel Software’s blog, in conjunction with Intel’s Ultimate Coder Challenge. Keep checking back to read our latest updates!
Week three seemed to go much better for the Infrared5 team. We are back on our feet with head tracking, and despite the judges lack of confidence in our ability to track eyes, we still believe that we’ve got a decent chance of pulling it off. Yes, it’s true as Nicole as said in her post this week, that the Intel Perceptual Computing (IPC) SDK isn’t yet up to the task. She had an interview with the Perceptual computing team and they told her “that eye tracking was going to be implemented later”. What’s funny about the lack of eye tracking and even decent gaze tracking in the IPC SDK is that the contest is showing this:

Yes we know it’s just marketing, but it is a pretty misleading image. They have a 3D mesh over a guy’s face giving the impression that the SDK can do AAM and POSIT. That would be so cool!  Look out FaceAPI! Unfortunately it totally doesn’t do that. At least not yet.

This isn’t to say that Intel is taking a bad approach with the IPC SDK beta either. They are trying out a lot of things at once and not getting lost in the specifics of just a few features. This allows developers to tell them what they want to do with it without spending tremendous effort on features that wouldn’t even be used.

The lack of decent head, gaze and eye tracking is what’s inspired us on to eventually release our tracking code as open source. Our hope is that future developers can leverage our work on these features and not have to go through the pain we did in this contest. Maybe Intel will just merge our code into the IPC SDK and we can continue to make the product better together.

Another reason we are sticking with our plan on gaze and eye tracking is that we feel strongly, as do the judges, that these features are some of the most exciting aspects of the perceptual computing camera. A convertible ultrabook has people’s hands busy with typing, touch gestures, etc. and having an interface that works using your face is such a natural fit for this kind of setup.

Latest Demo of Kiwi Catapult Revenge

Check out the latest developments with the Unity Web Player version. We’ve added a new fireball/flamethrower style effect, updated skybox, sheep and more. Note that this is still far from final art and behavior for the game, but we want to continue showing the process we are going through by providing these snapshots of the game in progress. This build requires the free Brass Monkey app for iOS or Android.

A Polished Experience

In addition to being thoroughly entertained by the judges’ video blooper this week, one thing we heard consistently from them is that they were expecting more polished apps from the non-individual teams. We couldn’t agree more! One advantage that we have in the contest is that we have a fantastic art and game design team. That’s not to say our tech skills are lacking either. We are at our core a very technically focused company, but we tend not to compartmentalize the design process and the technology implementation in projects we take on. Design and technology have to work together in harmony to create an amazing user experience, and that’s exactly what we’re doing in this challenge.

Game design is a funny, flexible and agile process. What you set out to do in the beginning rarely ends up being what you make in the end. Our initial idea started as a sort of Mad Max road warrior style driving and shooting game (thus Sascha thinking ours was a racing game early on), but after having read some bizarre news articles on eradicating cats in New Zealand we decided the story of Cats vs. Kiwis should be the theme. Plus Rebecca and Aaron really wanted to try out this 2D paper, pop-up book style, and the Kiwi story really lends itself to that look.

Moving to this new theme kept most of the core game mechanics as the driving game. Tracking with the head and eyes to shoot and using the phone as a virtual steering wheel are exactly the same in the road warrior idea. Since our main character Karl Kiwi has magical powers and can fly, we made it so he would be off the ground (unlike a car that’s fixed to the ground). Another part of the story is that Karl can breathe fire like a dragon, so we thought that’s an excellent way to use the perceptual computing camera by having the player open their mouth to be able to shoot fire. Shooting regular bullets didn’t work with the new character either, so we took some inspiration from funny laser cats memes, SNL and decided that he should be able to shoot lasers from his eyes. Believe it or not, we have been wanting to build a game involving animals and lasers for a while now. “Invasion of the Killer Cuties” was a game we concepted over two years ago where you fly a fighter plane in space against cute rodents that shoot lasers from their eyes (initial concept art shown below).

Since Chris wrote up the initial game design document (GDD) for Kiwi Catapult Revenge there have been plenty of other changes we’ve made throughout the contest. One example: our initial pass at fire breathing (a spherical projectile) wasn’t really getting the effect we wanted. In the GDD it was described as a fireball so this was a natural choice. What we found though is that it was hard to hit the cats, and the ball didn’t look that good either. We explored how dragon fire breathing is depicted in movies, and the effect is much more like how a flamethrower works. The new fire breathing effect that John implemented this week is awesome! And we believe it adds to the overall polish of our entry for the contest.

(image credit MT Falldog)

Another aspect of the game that wasn’t really working so far was that the main character was never shown. We chose a first person point of view so that the effect of moving your head and peering around items would feel incredibly immersive, giving the feeling that you are really right in this 3D world. However, this meant that you would never see Karl, our protagonist.

Enter the rear view mirror effect. We took a bit of inspiration from the super cool puppets that Sixense showed last week, and this video of an insane wingsuit base jump and came up with a way to show off our main character. Karl Kiwi will be fitted with a rear view mirror so that he can see what’s behind him, and you as the player can the character move the same as you. When you tilt your head, Karl will tilt his, when you look right, so will Karl, and when you open your mouth Karl’s beak will open. This will all happen in real time, and the effect will really show the power of the perceptual computing platform that Intel has provided.

Head Tracking Progress Plus Code and Videos

It wouldn’t be a proper Ultimate Coder post without some video and some code, so we have provided you some snippets for your perusal. Steff did a great job of documenting his progress this week, and we want to show you step by step where we are heading by sharing a bit of code and some video for each of these face detection examples. Steff is working from this plan, and knocking off each of the individual algorithms step by step. Note that this week’s example requires the OpenCV library and a C compiler for Windows.

This last week of Steff’s programming was all about two things: 1) switching from working entirely in Unity (with C#) to a C++ workflow in Visual Studio, and 2) refining our face tracking algorithm.  As noted in last week’s post, we hit a roadblock trying to write everything in C# in Unity with DLL for the Intel SDK and OpenCV.  There were just limits to the port of OpenCV that we needed to shed.  So, we spent some quality time setting up in VS 2012 Express and enjoying the sharp sting of pointers, references, and those type of lovely things that we have avoided by working in C#.  However there is good news, we did get back the amount of lower level control needed to refine face detection!

Our main refinement this week was to break through the limitations of tracking faces that we encountered when implementing the Viola-Jones detection method using Haar Cascades. This is a great way to find a face, but it’s not the best for tracking a face from frame to frame.  It has limitations in orientation; e.g. if the face is tilted to one side the Haar Cascade no longer detects a face.  Another drawback is that while looking for a face, the algorithm is churning through images per every set block of pixels.  It can really slow things down. To break through this limitation, we took inspiration from the implementation by the team at ROS.org . They have done a nice job putting face tracking together using python, OpenCV, and an RGB camera + Kinect. Following their example, we have implemented feature detection with GoodFeaturesToTrack and then tracked each feature from frame to frame using Optical Flow. The video below shows the difference between the two methods and also includes a first pass at creating a blue screen from the depth data.

This week, we will be adding depth data into this tracking algorithm.  With depth, we will be able to refine our Region Of Interest to include an good estimate of face size and we will also be able to knock out the background to speed up Face Detection with the Haar Cascades. Another critical step is integrating our face detection algorithms into the Unity game. We look forward to seeing how all this goes and filling you in with next week’s post!

We are also really excited about all the other teams’ progress so far, and in particular we want to congratulate Lee on making a super cool video last week!  We had some plans to do a more intricate video based on Lee’s, but a huge snowstorm in Boston put a bit of a wrench in those plans. Stay tuned for next week’s post though, as we’ve got some exciting (and hopefully funny) stuff to show you!

For you code junkies out there, here is a code snippet showing how we implemented GoodFeaturesToTrack and Lucas-Kanada Optical Flow:

#include "stdafx.h"

#include "cv.h"

#include "highgui.h"

#include <stdio.h>

#include <stdlib.h>

#include <string.h>

#include <assert.h>

#include <math.h>

#include <float.h>

#include <limits.h>

#include <time.h>

#include <ctype.h>

#include <vector>

#include "CaptureFrame.h"

#include "FaceDetection.h"

using namespace cv;

using namespace std;

static void help()


// print a welcome message, and the OpenCV version

cout << "\nThis is a demo of Robust face tracking use Lucas-Kanade Optical Flow,\n"

"Using OpenCV version %s" << CV_VERSION << "\n"

<< endl;

cout << "\nHot keys: \n"

"\tESC - quit the program\n"

"\tr - restart face tracking\n" << endl;


// function declaration for drawing the region of interest around the face

void drawFaceROIFromRect(IplImage *src, CvRect *rect);

// function declaration for finding good features to track in a region

int findFeatures(IplImage *src, CvPoint2D32f *features, CvBox2D roi);

// function declaration for finding a trackbox around an array of points

CvBox2D findTrackBox(CvPoint2D32f *features, int numPoints);

// function declaration for finding the distance a point is from a given cluster of points

int findDistanceToCluster(CvPoint2D32f point, CvPoint2D32f *cluster, int numClusterPoints);

// Storage for the previous gray image

IplImage *prevGray = 0;

// Storage for the previous pyramid image

IplImage *prevPyramid = 0;

// for working with the current frame in grayscale

IplImage *gray = 0;

// for working with the current frame in grayscale2 (for L-K OF)

IplImage *pyramid = 0;

// max features to track in the face region

int const MAX_FEATURES_TO_TRACK = 300;

// max features to add when we search on top of an existing pool of tracked points

int const MAX_FEATURES_TO_ADD = 300;

// min features that we can track in a face region before we fail back to face detection

int const MIN_FEATURES_TO_RESET = 6;

// the threshold for the x,y mean squared error indicating that we need to scrap our current track and start over

float const MSE_XY_MAX = 10000;

// threshold for the standard error on x,y points we're tracking

float const STANDARD_ERROR_XY_MAX = 3;

// threshold for the standard error on x,y points we're tracking

double const EXPAND_ROI_INIT = 1.02;

// max distance from a cluster a new tracking can be

int const ADD_FEATURE_MAX_DIST = 20;

int main(int argc, char **argv)


// Init some vars and const

// name the window

const char *windowName = "Robust Face Detection v0.1a";

// box for defining the region where a face was detected

CvRect *faceDetectRect = NULL;

// Object faceDetection of the class "FaceDetection"

FaceDetection faceDetection;

// Object captureFrame of the class "CaptureFrame"

CaptureFrame captureFrame;

// for working with the current frame

IplImage *currentFrame;

// for testing if the stream is finished

bool finished = false;

// for storing the features

CvPoint2D32f features[MAX_FEATURES_TO_TRACK] = {0};

// for storing the number of current features that we're tracking

int numFeatures = 0;

// box for defining the region where a features are being tracked

CvBox2D featureTrackBox;

// multiplier for expanding the trackBox

float expandROIMult = 1.02;

// threshold number for adding more features to the region

int minFeaturesToNewSearch = 50;

// Start doing stuff ------------------>

// Create a new window

cvNamedWindow(windowName, 1);

// Capture from the camera


// initialize the face tracker


// capture a frame just to get the sizes so the scratch images can be initialized

finished = captureFrame.CaptureNextFrame();

if (finished)




return 0;


currentFrame = captureFrame.getFrameCopy();

// init the images

prevGray = cvCreateImage(cvGetSize(currentFrame), IPL_DEPTH_8U, 1);

prevPyramid = cvCreateImage(cvGetSize(currentFrame), IPL_DEPTH_8U, 1);

gray = cvCreateImage(cvGetSize(currentFrame), IPL_DEPTH_8U, 1);

pyramid = cvCreateImage(cvGetSize(currentFrame), IPL_DEPTH_8U, 1);

// iterate through each frame



// check if the video is finished (kind of silly since we're only working on live streams)

finished = captureFrame.CaptureNextFrame();

if (finished)




return 0;


// save a reference to the current frame

currentFrame = captureFrame.getFrameCopy();

// check if we have a face rect

if (faceDetectRect)


// Create a grey version of the current frame

cvCvtColor(currentFrame, gray, CV_RGB2GRAY);

// Equalize the histogram to reduce lighting effects

cvEqualizeHist(gray, gray);

// check if we have features to track in our faceROI

if (numFeatures > 0)


bool died = false;

//cout << "\nnumFeatures: " << numFeatures;

// track them using L-K Optical Flow

char featureStatus[MAX_FEATURES_TO_TRACK];

float featureErrors[MAX_FEATURES_TO_TRACK];

CvSize pyramidSize = cvSize(gray->width + 8, gray->height / 3);

CvPoint2D32f *featuresB = new CvPoint2D32f[MAX_FEATURES_TO_TRACK];

CvPoint2D32f *tempFeatures = new CvPoint2D32f[MAX_FEATURES_TO_TRACK];

cvCalcOpticalFlowPyrLK(prevGray, gray, prevPyramid, pyramid, features, featuresB, numFeatures, cvSize(10,10), 5, featureStatus, featureErrors, cvTermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, -3), 0);

numFeatures = 0;

float sumX = 0;

float sumY = 0;

float meanX = 0;

float meanY = 0;

// copy back to features, but keep only high status points

// and count the number using numFeatures

for (int i = 0; i < MAX_FEATURES_TO_TRACK; i++)


if (featureStatus[i])


// quick prune just by checking if the point is outside the image bounds

if (featuresB[i].x < 0 || featuresB[i].y < 0 || featuresB[i].x > gray->width || featuresB[i].y > gray->height)


// do nothing




// count the good values

tempFeatures[numFeatures] = featuresB[i];


// sum up to later calc the mean for x and y

sumX += featuresB[i].x;

sumY += featuresB[i].y;



//cout << "featureStatus[" << i << "] : " << featureStatus[i] << endl;


//cout << "numFeatures: " << numFeatures << endl;

// calc the means

meanX = sumX / numFeatures;

meanY = sumY / numFeatures;

// prune points using mean squared error

// caclulate the squaredError for x, y (square of the distance from the mean)

float squaredErrorXY = 0;

for (int i = 0; i < numFeatures; i++)


squaredErrorXY += (tempFeatures[i].x - meanX) * (tempFeatures[i].x - meanX) + (tempFeatures[i].y  - meanY) * (tempFeatures[i].y - meanY);


//cout << "squaredErrorXY: " << squaredErrorXY << endl;

// calculate mean squared error for x,y

float meanSquaredErrorXY = squaredErrorXY / numFeatures;

//cout << "meanSquaredErrorXY: " << meanSquaredErrorXY << endl;

// mean squared error must be greater than 0 but less than our threshold (big number that would indicate our points are insanely spread out)

if (meanSquaredErrorXY == 0 || meanSquaredErrorXY > MSE_XY_MAX)


numFeatures = 0;

died = true;




// Throw away the outliers based on the x-y variance

// store the good values in the features array

int cnt = 0;

for (int i = 0; i < numFeatures; i++)


float standardErrorXY = ((tempFeatures[i].x - meanX) * (tempFeatures[i].x - meanX) + (tempFeatures[i].y - meanY) * (tempFeatures[i].y - meanY)) / meanSquaredErrorXY;

if (standardErrorXY < STANDARD_ERROR_XY_MAX)


// we want to keep this point

features[cnt] = tempFeatures[i];




numFeatures = cnt;

// only bother with fixing the tail of the features array if we still have points to track

if (numFeatures > 0)


// set everything past numFeatures to -10,-10 in our updated features array

for (int i = numFeatures; i < MAX_FEATURES_TO_TRACK; i++)


features[i] = cvPoint2D32f(-10,-10);




// check if we're below the threshold min points to track before adding new ones

if (numFeatures < minFeaturesToNewSearch)


// add new features

// up the multiplier for expanding the region


// expand the trackBox

float newWidth = featureTrackBox.size.width * expandROIMult;

float newHeight = featureTrackBox.size.height * expandROIMult;

CvSize2D32f newSize = cvSize2D32f(newWidth, newHeight);

CvBox2D newRoiBox = {featureTrackBox.center, newSize, featureTrackBox.angle};

// find new points

CvPoint2D32f additionalFeatures[MAX_FEATURES_TO_ADD] = {0};

int numAdditionalFeatures = findFeatures(gray, additionalFeatures, newRoiBox);

int endLoop = MAX_FEATURES_TO_ADD;

if (MAX_FEATURES_TO_TRACK < endLoop + numFeatures)

endLoop -= numFeatures + endLoop - MAX_FEATURES_TO_TRACK;

// copy new stuff to features, but be mindful of the array max

for (int i = 0; i < endLoop; i++)


// TODO check if they are way outside our stuff????

int dist = findDistanceToCluster(additionalFeatures[i], features, numFeatures);



features[numFeatures] = additionalFeatures[i];




// TODO check for duplicates???

// check if we're below the reset min

if (numFeatures < MIN_FEATURES_TO_RESET)


// if so, set to numFeatures 0, null out the detect rect and do face detection on the next frame

numFeatures = 0;

faceDetectRect = NULL;

died = true;





// reset the expand roi mult back to the init

// since this frame didn't need an expansion



// find the new track box

if (!died)

featureTrackBox = findTrackBox(features, numFeatures);




// convert the faceDetectRect to a CvBox2D

CvPoint2D32f center = cvPoint2D32f(faceDetectRect->x + faceDetectRect->width * 0.5, faceDetectRect->y + faceDetectRect->height * 0.5);

CvSize2D32f size = cvSize2D32f(faceDetectRect->width, faceDetectRect->height);

CvBox2D roiBox = {center, size, 0};

// get features to track

numFeatures = findFeatures(gray, features, roiBox);

// verify that we found features to track on this frame

if (numFeatures > 0)


// find the corner subPix

cvFindCornerSubPix(gray, features, numFeatures, cvSize(10, 10), cvSize(-1,-1), cvTermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03));

// define the featureTrackBox around our new points

featureTrackBox = findTrackBox(features, numFeatures);

// calculate the minFeaturesToNewSearch from our detected face values

minFeaturesToNewSearch = 0.9 * numFeatures;

// wait for the next frame to start tracking using optical flow




// try for a new face detect rect for the next frame

faceDetectRect = faceDetection.detectFace(currentFrame);






// reset the current features

numFeatures = 0;

// try for a new face detect rect for the next frame

faceDetectRect = faceDetection.detectFace(currentFrame);


// save gray and pyramid frames for next frame

cvCopy(gray, prevGray, 0);

cvCopy(pyramid, prevPyramid, 0);

// draw some stuff into the frame to show results

if (numFeatures > 0)


// show the features as little dots

for(int i = 0; i < numFeatures; i++)


CvPoint myPoint = cvPointFrom32f(features[i]);

cvCircle(currentFrame, cvPointFrom32f(features[i]), 2, CV_RGB(0, 255, 0), CV_FILLED);


// show the tracking box as an ellipse

cvEllipseBox(currentFrame, featureTrackBox, CV_RGB(0, 0, 255), 3);


// show the current frame in the window

cvShowImage(windowName, currentFrame);

// wait for next frame or keypress

char c = (char)waitKey(30);

if(c == 27)




case 'r':

numFeatures = 0;

// try for a new face detect rect for the next frame

faceDetectRect = faceDetection.detectFace(currentFrame);




// Release the image and tracker


// Destroy the window previously created


return 0;


// draws a region of interest in the src frame based on the given rect

void drawFaceROIFromRect(IplImage *src, CvRect *rect)


// Points to draw the face rectangle

CvPoint pt1 = cvPoint(0, 0);

CvPoint pt2 = cvPoint(0, 0);

// setup the points for drawing the rectangle

pt1.x = rect->x;

pt1.y = rect->y;

pt2.x = pt1.x + rect->width;

pt2.y = pt1.y + rect->height;

// Draw face rectangle

cvRectangle(src, pt1, pt2, CV_RGB(255,0,0), 2, 8, 0 );


// finds features and stores them in the given array

// TODO move this method into a Class

int findFeatures(IplImage *src, CvPoint2D32f *features, CvBox2D roi)


//cout << "findFeatures" << endl;

int featureCount = 0;

double minDistance = 5;

double quality = 0.01;

int blockSize = 3;

int useHarris = 0;

double k = 0.04;

// Create a mask image to be used to select the tracked points

IplImage *mask = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);

// Begin with all black pixels


// Create a filled white ellipse within the box to define the ROI in the mask.

cvEllipseBox(mask, roi, CV_RGB(255, 255, 255), CV_FILLED);

// Create the temporary scratchpad images

IplImage *eig = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);

IplImage *temp = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);

// init the corner count int

int cornerCount = MAX_FEATURES_TO_TRACK;

// Find keypoints to track using Good Features to Track

cvGoodFeaturesToTrack(src, eig, temp, features, &cornerCount, quality, minDistance, mask, blockSize, useHarris, k);

// iterate through the array

for (int i = 0; i < cornerCount; i++)


if ((features[i].x == 0 && features[i].y == 0) || features[i].x > src->width || features[i].y > src->height)


// do nothing







//cout << "\nfeatureCount = " << featureCount << endl;

// return the feature count

return featureCount;


// finds the track box for a given array of 2d points

// TODO move this method into a Class

CvBox2D findTrackBox(CvPoint2D32f *points, int numPoints)


//cout << "findTrackBox" << endl;

//cout << "numPoints: " << numPoints << endl;

CvBox2D box;

// matrix for helping calculate the track box

CvMat *featureMatrix = cvCreateMat(1, numPoints, CV_32SC2);

// collect the feature points in the feature matrix

for(int i = 0; i < numPoints; i++)

cvSet2D(featureMatrix, 0, i, cvScalar(points[i].x, points[i].y));

// create an ellipse off of the featureMatrix

box = cvFitEllipse2(featureMatrix);

// release the matrix (cause we're done with it)


// return the box

return box;


int findDistanceToCluster(CvPoint2D32f point, CvPoint2D32f *cluster, int numClusterPoints)


int minDistance = 10000;

for (int i = 0; i < numClusterPoints; i++)


int distance = abs(point.x - cluster[i].x) + abs(point.y - cluster[i].y);

if (distance < minDistance)

minDistance = distance;


return minDistance;


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Face Tracking, Depth Sensors, Flying and Art = Progress!

February 28th, 2013 by admin

This post was featured on Intel Software’s blog, in conjunction with Intel’s Ultimate Coder Challenge. Keep checking back to read our latest updates!

This week our team made a ton of progress on our game Kiwi Catapult Revenge. In their posts, the judges made some suggestions that the projects we are undertaking are ambitious and are perhaps a bit more than what can be accomplished in seven weeks. We have to agree that none of the teams are taking the easy way out, and we feel that because everyone is taking on such lofty goals it will only spur on more creativity from the entire group of competitors. Lucky for us, the Infrared5 guys/gals are accustomed to tight deadlines, insane schedules and hard to achieve deliverables, so the Ultimate Coder Challenge just feels like a demanding client. But it’s not like this is going to be easy. We’ve got quite a few challenges and areas of risk that we need to keep an eye on, plus it’s so tempting to continue to add scope, especially when you have the “fun factor” element which is so essential to creating a good game.

Speaking of the competitors, what many people following the contest may not realize is that we actually all are in communication quite a bit. We’ve got our own informal mailing list going, and there’s a lot of back and forth between the teams and sharing of ideas across projects. There is more of a collaborative spirit rather than a cut-throat competitive nature amongst the teams. We’ve even got a Google Hangout session scheduled this week so that we can all talk face to face. Unfortunately, Lee’s super cool video chat program isn’t ready for the task. We, at Infrared5, strongly believe that sharing ideas spurs on innovation and it will up our game to be so open with the other teams competing. After all, great ideas don’t happen in a vacuum.

In addition to our post this week, we did a quick video where Chris talked to our team members about head tracking, art and more.

Face Tracking

Let’s start with the biggest challenge we’ve given ourselves: Face tracking. We have been playing with OpenCV and the Intel Perceptual Computing SDK in different Unity proof of concept projects last week. Looking at our projected plan that we created at the start of the competition, our focus was on implementing basic face tracking by detecting Haar-like features. This works well, but he face detection algorithm currently has limits. If the target face is rotated to too far to either side then it will not be recognized and tracked as a “face.” Fortunately, we are aware of the limitation in the algorthim and have plans to implement a patch. We created Obama and Beyonce controllers so those of us with beards (Steff) can have more faces to test without bothering anyone in the office to “come and put your face in front of this screen.” Our current setup will cause issues if you have a beard and wear a hat – foreheads and mouths are important with this algorithm! Check out our “custom controllers” below.

Best news of the week: the depth sensing camera is awesome! It gives much better detail than we originally saw with the samples that came packaged with the SDK. The not-as-good news: since this SDK is still in beta, the documentation is not so awesome. Things do not always match up, especially with the prepackaged port to Unity3d. We are experiencing a good amount of crashing and might have to back out of this and write some of our own C code to port in the methods that we need for mapping the depth data to the RGB data. Stay tuned for what happens there!

Back to the good news. We originally were only going to use the data from the depth sensor to wipe out the background (one of the first steps in our planned pipeline). However, the depth data is so good, it will definitely also help us later on when we are calculating the pose of the player’s head. Pose calculations depend on estimating the position of non-coplanar points (read up on POSIT if you really want to geek-out now, but we will fill in more detail on POSIT once we implement it in our system), and finding these points is going to be much less of an iterative process with this depth data since we can actually look up the depth and associated confidence level for any point in the RGB image!

Introducing Gaze Tracking

Because of all the details from the depth + RBG cameras, we are optimistic that we will be able to track the player’s pupils. This of course means that we will be able to get gaze tracking working with our game. In Kiwi Catapult Revenge aiming at your targets won’t just lock into the center of where you are tilting your head, but we will allow you to fire precisely where you are looking, at any point in time. This one feature combined with the head tilt is where you start to really see how video games based on perceptual computing are going to have tremendous performance advantages over typical game controls like joypads, keyboard/mouse, etc. Now imagine adding another sensor device to the mix like Google Glass. What would be possible then? Maybe we can convince Google to give us early access to find out.

Game Engine

John has made a ton of progress on the game mechanics this week. He’s got a really good flow for flying in the game. We took inspiration from the PS3 game Flower for the player character movement we wanted to create in Kiwi Catapult Revenge. There’s a nice bounce and easing to the movement, and the ability to subtly launch over hills and come back down smoothly is going to really bring the flying capabilities of Karl Kiwi to life. John managed to get this working in a demo along with head tracking (currently mapped to the mouse movement). You can fly around (WASD keys), and look about, and get a general feel for how the game is going to play. We’ve posted a quick Unity Web Player version (here) of the demo for you to try out. Keep in mind that the controls aren’t yet mapped to the phone, nor is the artwork even close to final in this version.

Art and Game Design

Speaking of artwork, Rebecca, Aaron and Elena have been producing what everyone seems to agree is amounting to a very unique and inspiring visual aspect to our game. Chris did a quick interview with Rebecca and Aaron on the work they are doing and what inspired them to come up with the idea. We’ve included that in our video this week as well.

This week the design team plans to experiment more with paper textures and lighting, as well as rigging up some of the characters for some initial looks at animation and movement in the game.

Oh, and in case you missed it, we posted an update on the game with the background story of our main character. There you can also find some great concept art from Aaron and an early version of the 3D environment to whet your appetite.

That’s it for this week. What do you think about the playable game demo? What about our approach to face tracking? Is there anything else that we should be considering? Please let us know what you think in the comments below.

Plan of Attack and Face Tracking Challenges Using the Intel Perceptual Computing SDK

February 19th, 2013 by Chris Allen

This post was featured on Intel Software’s blog, in conjunction with Intel’s Ultimate Coder Challenge. Keep checking back to read our latest updates!

We are very excited to be working with the Intel Perceptual Computing (IPC) SDK and to be a part of the Ultimate Coder Challenge! The new hardware and software that Intel and its partners have created allows for some very exciting possibilities. It’s our goal to really push the boundaries of what’s possible using the technology. We believe that perceptual computing plays a huge role in the future of human-to-computer interaction, and isn’t just a gimmick shown in movies like Minority Report. We hope to prove out some of the ways that it can actually improve the user experience with the game that we are producing for the competition.

Before we begin with the bulk of this post, we should cover a little bit on the makeup of our team and the roles that each of us play on the project. Unlike many of the teams in the competition, we aren’t a one man show, so each of our members play a vital role in creating the ultimate application. Here’s a quick rundown of our team:

Kelly Wallick – Project Manager

Chris Allen – Producer, Architect and Game Designer
Steff Kelsey – Tech Lead, Engineer focusing on the Intel Perceptual Computing SDK inputs
John Grden – Game Developer focusing on the Game-play

Rebecca Allen – Creative Director
Aaron Artessa – Art Director, Lead Artist, Characters, effects, etc.
Elena Ainley – Environment Artist and Production Art

When we first heard about the idea of the competition we started thinking about ways that we could incorporate our technology (Brass Monkey) with the new 3D image tracking inputs that Intel is making accessible to developers. Most of the examples being shown with the Perceptual Computing SDK focus on hand gestures, and we wanted to take on something a bit different. After much deliberation we arrived at the idea of using the phone as a tilt-based game controller input, and using head and eye tracking to create a truly immersive experience. We strongly believe that this combination will make for some very fun game play.

Our art team was also determined not to make the standard 3D FPS shoot-em-up game that we’ve seen so many times before, so we arrived at a very creative use of the tech with a wonderful background story of a young New Zealand Kiwi bird taking revenge on the evil cats that killed his family. To really show off the concept of head-tracking and peering around items in the world, we decided on a paper cutout art style. Note that this blog post and the other posts we will be doing on the Ultimate Coder site are really focused on the technical challenges and processes we are taking, and much less on the art and game design aspects of the project. After all, the competition is call the Ultimate Coder, not the Ultimate Designer. If you are interested in the art and design of our project, and we hope that you are, then you should follow our posts on our company’s blogs that will be covering much more of those details. We will be sure to reference these posts on every blog post here as well so that you can find out more about the entire process we are undertaking.

The name of the game that we’ve come up with for the competition is called Kiwi Catapult Revenge.

So with that, let’s get right to the technical nitty gritty.

Overview of the Technology We are Using


As we wanted to make a 3D game for the competition we decided to use Unity as our platform of choice. This tool allows for fast prototyping, ease of art integration and much more. We are also well versed in using Unity for a variety of projects at Infrared5, and our Brass Monkey SDK support for it is very polished.

Brass Monkey

We figured that one of our unique advantages in the competition would be to make use of the technology that we created. Brass Monkey SDK for Unity allows us to turn the player’s smartphone into a game controller for Unity games. We can leverage the accelerometers, gyroscopes and touch screens of the device as another form of input to the game. In this case, we want to allow for steering your Kiwi bird through the environment using tilt, and allow firing and control of the speed via the touch screen on the player’s phone.

Intel Perceptual Computing SDK

We decided to leverage the ICP SDK for head tracking, face recognition and possibly more. In the case of Kiwi Catapult Revenge the payer will use his eyes for aiming (the player character can shoot lasers from his eyes). The environment will also shift according to the angle in which the user is viewing causing the scene to feel like real 3D. Take a look at this example using a Wiimote for a similar effect that we are going for. In addition, our player can breath fire by opening his or her mouth in the shape of an “o” and pressing the fire button on the phone.

There are certainly other aspects of the SDK we hope to leverage, but we will leave those for later posts.


We are going to use this C-based library for more refined face tracking algorithms. Read more to find out why we chose OpenCV(opencv.org) to work in conjunction with the IPC SDK. Luckily, OpenCV is also developed by Intel, so hopefully that gets us additional points for using two of Intel’s libraries.

Head Tracking

The biggest risk item in our project is getting head tracking that performs well enough to be a smooth experience in game play, so we’ve decided to tackle this early on.

When we first started looking at the examples that shipped with the IPC SDK there were very few dealing with head tracking. In fact it was really only in the latest release where we found anything that was even close to what we proposed to build. That, and it was in this release that they exposed these features to the Unity version of the SDK. What we found are examples that simply don’t perform very well. They are slow, not all that accurate, and unfortunately just won’t cut it for the experience we are shooting for.

To make matters worse, the plugin for Unity is very limited. It didn’t allow us to manipulate much, if anything, with regards to head tracking or face recognition algorithms. As a Unity developer you either have to accept the poor performing pre-canned versions of the algorithms the SDK exposes, or get the raw data from the camera and do all the calculations yourself. What we found is that face tracking with what they provide gives us sub 3 frame per second performance that wasn’t very accurate. Now to be clear, the hand gesture features are really very polished, and work well in Unity.  It seems that Intel’s priority has been on those features, and head/face detection is lagging very much behind. This presents a real problem for our vision of the game, and we quickly realized that we were going to have to go about it differently if we were going to continue with our idea.


When we realized the current version of the IPC SDK wasn’t going to cut it by itself, we started looking into alternatives. Chris had done some study of OpenCV (CV stands for computer vision) a while back, and he had a book on the subject. He suggested that we take a look at that library to see if anyone else had written more effective head and eye tracking algorithms using that tool-set. We also discovered what looked like a very polished and effective head tracking library called OpenTL . We got very excited with what we saw, but when we went to download the library, we discovered the OpenTL isn’t so open after all. It’s not actually open source software, and we didn’t want to get involved with licensing a 3rd party tool for the competition. Likewise the FaceAPI from SeeingMachines looked very promising, but it also carried with it a proprietary license in order for us to use it.  Luckily what we found using OpenCV appeared to be more than capable of doing the job.

Since OpenCV is a C library we needed to figure out how to get it to work within Unity. We knew that we would need to compile a dll that would expose the functions to the Mono based Unity environment, or find a version out on the Internet that had already done this. Luckily we found this example, and incorporated it into our plans.

Use of the Depth Camera

The other concern we had was that all the examples we saw of face tracking in real-time didn’t make use of any special camera. They all used a simple webcam, and we really wanted to leverage the unique hardware that Intel provided us for the challenge. One subtle thing that we noticed with most of the examples we saw was they performed way better with the person in front of a solid background. The less noise the image had the better it would perform. So, we thought, why not use the depth sensor to block out anything behind the user’s head, essentially guaranteeing less noise in our images being processed regardless of what’s behind our player. This would be a huge performance boost over traditional webcams!

Application Flow and Architecture

After carefully considering our tools we finally settled on an architecture that spelled out how all the pieces would work together. We would use the Unity IPC SDK for the camera frames as raw images, and for getting the depth sensor data to block out only the portions of the image that had the person’s head. We would then leverage OpenCV for face tracking algorithms via a plugin to Unity.

We will be experimenting with a few different combinations of algorithms until we find something that give us the performance we need to implement as a game controller and (hopefully) also satisfy the desired feature set of tracking the head position and rotation, identifying if the mouth is open or closed, and tracking the gaze direction of the eyes.  Each step in the process is done to set up the the steps that follow.

In order to detect the general location of the face, we propose to use the Viola-Jones detection method.  The result of this method will be a smaller region of interest (ROI) for mouth and eye detection algorithms to sort through.

There are few proposed methods to track the facial features and solve for the rotation of the head.  The first method is to take use the results from the first pass to define 3 new ROIs and to search specifically for the mouth and the eyes using sets of comparative images designed specifically for the task.  The second method is to use the Active Appearance Model (AAM) to find match a shape model of facial features in the region.  We will go into more detail about these methods in future posts after we attempt them.

Tracking the gaze direction will be done by examining the ROI for each eye and determining the location of the iris and pupil by the Adaptive EigenEye method.

Trackable points will be constrained with Lucas-Kanade optical flow.  The optical flow compares the previous frame with the current one and finds the most likely locations of tracked points using a least squares estimation.

Summing it Up

We believe that we’ve come up with an approach that leverages the unique capabilities of the Perceptual Computing Camera and actually adds to the user experience of our game concept. As we start in on the development it’s going to be interesting to see how much this changes over the next seven weeks. We already have several questions about how it’s going to go: How much did we think would actually work will? What performance tuning will we need to do? How many of the other features of the IPC SDK can we leverage to make our game more engaging? Will we have enough time to pull off such an ambitious project in such a short time frame?

Wow! That turned out to be a long post! Thanks for taking the time to read what we are up to.

We are also curious to hear from you, other developers out there. What would you do differently given our goals for the project? If you’ve got experience with computer vision algorithms, or even just want to chime in with your support, we would love to hear from you!

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Seven weeks. Seven teams. ONE ULTIMATE APP!

February 6th, 2013 by Rosie

Infrared5 and Brass Monkey are excited to announce their participation in Intel Software’s Ultimate Coder Challenge, ‘Going Perceptual’! The IR5/Brass Monkey team, along with six other teams from across the globe, will be competing in this seven week challenge to build the ultimate app. The teams will be using the latest Ultrabook convertible hardware, along with the Intel Perceptual Computing SDK and camera to build the prototype. The competitors range from large teams to individual developers, and each will take a unique approach to the challenge. The question will be which team or individual can execute their vision with the most success under such strict time constraints?

Here at Infrared5/Brass Monkey headquarters, we have our heads in the clouds and our noses to the grindstone. We are dreaming big, hoping to create a game that will take user experience to the next level. We are combining game play experiences like those available on Nintendo’s Wii U and Microsoft Kinect. The team will use the Intel Perceptual Computing SDK for head tracking, which will allow the player to essentially peer into the tablet/laptop screen like a window. The 3D world will change as the player moves his head. We’ve seen other experiments that do this with other technology and think it is really remarkable. This one using Wii-motes by Johnny Lee is one of the most famous. Our team will be exploring this effect and other uses of the Intel Perceptual Computing SDK combined with the Brass Monkey’s SDK (using a smartphone as a controller) to create a cutting edge, immersive experience. Not only that, but our creative team is coming up with all original IP to showcase the work.

Intel will feature documentation of the ups and downs of this process for each team, beginning February 15th. We will be posting weekly on our progress, sharing details about the code we are writing, and pointing out the challenges we face along the way. Be sure to check back here as the contest gets under way.

What would you build if you were in the competition? Let us know if you have creative ideas on how to use this technology; we would love to hear them.

We would like to thank Intel for this wonderful opportunity and wish our competitors the best of luck! Game on!

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GDC12 – Game Developer Conference 2012: a Post-Mortem

March 30th, 2012 by Elliott Mitchell

GDC12- AaaaaAAaaaAAAaaAAAAaAAAAA!!! (Force = Mass x Acceleration) by Dejoban Games and Owlchemy Labs, played by Oleg Pridiuk (Unity Technologies) as Ichiro Lambe (Dejobaan Games) and Deniz Opal (Cerebral Fix) watch - Photo Elliott Mitchell (Infrared5)

This year’s Game Developer Conference (GDC) 2012 was networking, networking and more networking.

Within a one mile proximity of the San Francisco Moscone Center, hordes of game developers and artists could be seen in the streets, cafes, bars, mall food courts, and hotel lobbies and heard talking shop, showing off their games, catching up with friends, debating the ethics of cloning social games from indies, shopping to find publishers, contractors and jobs. It was an intense meeting of the minds of people who make games in the streets of San Francisco.

Google Huddle chats, Google Groups email, shared Google Calendars and Twitter were all utilized very effectively to make the most of GDC. Multitudes of varied networking opportunities streamed in real-time through my iPhone 24/7. The level of my success at GDC was determined by how much networking I could possibly handle. With the help of my friends and the social/mobile networks,  success was at my fingertips.

In addition to the obsessive networking, there were many other valuable aspects of GDC. I’ll briefly highlight a few:

Jeff Ward’s Pre-GDC Board Game Night

GDC12- Elliott Mitchell (Infrared5), John Romero (Loot Drop), Brenda Garno Brathwaite (Loot Drop) & Elizabeth Sampat (Loot Drop) playing games at Jeff Ward's (Fire Hose Games) 3rd Annual Pre-GDC Board Game Night - Photo Drew Sikora

Jeff Ward (Fire Hose Games) knows how to get an amazing collection of game designers and developers together for a night playing board games. This was one of my favorite events of GDC. When else would I ever be able to play board games with John Romero (Loot Drop) and Brenda Garno Brathwaite (Loot Drop) while enjoying hors d’oeuvre and spirits? The crowd was a rich blend of artists, game developers, game designers, indies, students and superstars. There were so many new and classic games to play. I personally played Family Business and a really fun indie game prototype about operating a successful co-operative restaurant. Walking around after playing my games, I observed a host of other cool games being played and pitched. I’ll definitely be back for this event next year.

Independent Games Summit and Main Conference Sessions

GDC12 Ryan Creighton (Untold Entertainment) presenting Ponycorns: Catching Lightning in a Jar- Photo Elliott Mitchell (Infrared5)

Many session topics were super interesting but it wasn’t possible to attend all of them. Luckily, those with a GDC All-Access pass have access to the GDC Vault filled with recorded sessions. Here are a few sessions I saw which I found useful and interesting:

*Perhaps a Time of Miracles Was at Hand: The Business & Development of #Sworcery (Nathan Vella – Capy Games)

*The Pursuit of Indie Happiness: Making Great Games without Going Crazy (Aaron Isaksen – Indie Fund LLC)

*Ponycorns: Catching Lightning in a Jar (Ryan Creighton – Untold Entertainment)

*Light Probe Interpolation Using Tetrahedral Tessellations (Robert Cupisz – Unity Technologies)

Independent Game Festival Contestants on the Expo Floor

I played a bunch of the Independent Games Festival contestants’ games on the Expo floor

GDC12 - Alex Schwartz (Owlchemy Labs) playing Johann Sebastian Joust (Die Gute Fabrik) - Photo Elliott Mitchell (Infrared5)

before the festival winners are announced. There was a whole lot of innovation on display from this group. I particularly loved Johann Sebastian Joust (Die Gute Fabrik), a game without graphics, and Dear Esther (thechineseroom) which is stunning eye candy. Check out all the games here.

12th Annual Game Developer Choice Awards

I was super stoked to see two indies win big!

Superbrothers: Sword & Sorcery EP (Capy Games/Superbrothers) took the Best Handheld/Mobile Game award.

Johann Sebastian Joust (Die Gute Fabrik) won the Innovation Award.  Johann Sebastian Joust is worthy of it’s own blog post in the future.


* Unity booth – Cool tech from Unity and development venders partners showing off their wares
* Google Booth – Go Home Dinosaurs (Fire Hose Games) on Google Chrome
* Autodesk Booth (Maya and Mudbox)
* Indie Game Festival area ( All of it)

GDC12 - Chris Allen (Brass Monkey) and Andrew Kostuik (Brass Monkey) at the Unity Booth - Photo by Elliott Mitchell (Infrared5)


Lots of cool tech at the 1st Annual GDC Play. Our sister company, Brass Monkey, impressed onlookers with their Brass Monkey Controller for mobile devices and Play Brass Monkey web portal for both 2d and 3d games.


Last but not least, the most useful and pleasurable highlight of GDC was face time with the Unity Technology engineers and management. Sure, I’m on email, Skype, Twitter and Facebook with these guys but nothing is like face to face time with this crew. Time and access to Unity’s founders, engineers, evangelists and management is worth the price of GDC admission. Can’t wait until Unite 2012 in Amsterdam and GDC13 next March!

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Top 10 GDC Lists

March 1st, 2012 by Elliott Mitchell

GDC is approaching next week and I’ll be traveling to San Fransisco to participate in the epic game developer event. I’m psyched and here’s why:


10  The Expo Floor
9    The History Of 3D Games exhibit
8    Experimental Gameplay Sessions
7    The Unity Party
6    Indie Game: The Movie screening & Panel
5    GDC Play
4    14th Annual Independent Games Festival Awards
3    Networking, Networking & Networking
2    Independent Game Summit
1    Unity Technology Engineers


10  The Pursuit of Indie Happiness: Making Great Games without Going Crazy
9    Rapid, Iterative Prototyping Best Practices
8    Experimental Gameplay Sessions
7    Create New Genres (and Stop Wasting Your Life in the Clone Factories) [SOGS Design]
6    BURN THIS MOTHERFATHER! Game Dev Parents Rant
5    Bringing Large Scale Console Games to iOS Devices: A Technical Overview of The Bard’s Tale Adaptation
4    Light Probe Interpolation Using Tetrahedral Tessellations
3    Big Games in Small Packages: Lessons Learned In Bringing a Long-running PC MMO to Mobile
2    Art History for Game Devs: In Praise of Abstraction
1    Android Gaming on Tegra: The Future of Gaming is Now, and it’s on the Move! (Presented by NVIDIA)

If you’re going to be at GDC and want to talk shop with Infrared5 then please ping us! info (at) Infrared5 (dot) com

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