“From Paper to Pixels” interview featured on Intel blog

September 5th, 2013 by Adam Doucette

Recently our own Rebecca Allen, CEO and Creative Director at Infrared5, was featured in an interview by Wendy Boswell of Intel to discuss perceptual computing and the upcoming From Paper to Pixels event. Also featured in the post are two of the artists participating in this year’s event Anna Kristina Goransson and Rob Gonsalves. Follow the link and stay tuned for more details regarding the event, which will be held on September 21 & 22 here at the Infrared5 offices from the hours of 11:00 – 6:00.

Check out what Rebecca had to say!

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Multithreading Perceptual Computing Applications in Unity3d

August 21st, 2013 by Steff Kelsey

Once again we are excited about being featured on the Intel Developer Zone blog for a post done by Infrared5 Senior Developer Steff Kelsey. This post Multithreading Perceptual Computing Application in Unity3d talks about the challenges the Infrared5 team faced while participating in the Intel Ultimate Coder Challenge. Follow the link to hear the steps we went through to make Kiwi Catapult Revenge a reality! As always, we love hearing your feedback and feel free to share with others.

http://software.intel.com/en-us/blogs/2013/07/26/multithreading-perceptual-computing-applications-in-unity3d

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Kiwi Katapult Revenge Case Study by Intel

July 29th, 2013 by Adam Doucette

Earlier this year, Intel invited Infrared5 to compete in its Ultimate Coder Challenge: Going Perceptual, a groundbreaking contest that provided participants with an Ultrabook device, Creative Interactive Gesture Camera development kits, a still-evolving perceptual computing SDK, and all of the support possible for letting their imaginations run rampant. With its focus on sensor-based input, using Brass Monkey technology seemed a natural complement to perceptual computing, but the question surfaced of how to mesh the two. William Van Winkle recounts the story our team experienced in this challenging but fulfilling opportunity. As always, we welcome all questions and feedback. Enjoy!

http://software.intel.com/en-us/articles/infrared5-case-study

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Infrared5 Developer Steff Kelsey featured on Intel Developer Zone Blog

June 20th, 2013 by Adam Doucette

Recently our own Developer-extraordinaire Steff Kelsey (@thecodevik1ng) was featured on the Intel Developer Zone blog with his post – Masking RGB Inputs with Depth Data using the Intel PercC SDK and OpenCV. Steff and our team here at Infrared5 took part in Intel’s Ultimate Coder Challenge in the building of Kiwi Catapult Revenge using the Intel Perceptual Computing SDK. Check out Steff’s article below as he goes through the approaches used, what to look out for, and he provides some example code.

Steff Kelsey Intel Blog Post

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Infrared5 Participating in the Intel Ultimate Coder Challenge

April 18th, 2013 by admin

Perceptual Computing Challenge Winner to be Announced Next Week

Boston, MA – April 18, 2013 – Infrared5, an innovative Boston-based interactive studio, today
announced that the company is one of only seven developer teams participating in the Intel® Ultimate
Coder Challenge, and the only East Coast team.

The seven teams used the latest Intel Ultrabook™ devices, the Intel Perceptual Computing Software
Developers Kit (SDK), and Creative® Interactive Gesture Camera to build a special application prototype
in just seven weeks. The Ultimate Coder teams, including the Infrared5 team, blogged about their
experiences online and a panel of experts will crown the Ultimate Coder on April 24.

The Intel Ultimate Coder Challenge is designed to promote the use of perceptual computing. Perceptual
computing creates more immersive software applications by incorporating 2D/3D object tracking, user
gesture control, facial analysis and recognition, eye control, and voice recognition. Perceptual computing
is more than just the use of touch screens; it is a new area of pioneering development.

“The Intel Ultimate Coder Challenge provides a great opportunity for participants to innovate around
perceptual computing, and learn from peers in a few short weeks,” said Bob Duffy, community manager
for the Intel Developer Zone at Intel.

“We’re very comfortable with pioneering applications of technology,” said Rebecca Smith Allen, Infrared5
CEO and part of the Challenge team. “Perceptual computing is a new frontier we’re excited to explore.”

“The combination of the immersive properties of Brass Monkey and the power of perceptual computing
allowed our team to give the judges a unique experience that will hopefully earn us the Ultimate Coder
title,” said Chris Allen, the project leader. Allen is CEO of BrassMonkey, a next generation browser-based
game system created by the Infrared5 team.

In this game, the player uses a phone to fly a character around a 3D world. When game players turn
their head, the perceptual computing camera tracks what the player is looking at, causing the scene to
shift. This effect allows the player to peer around obstacles, giving the game a real sense of depth and
immersion.

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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

captureFrame.StartCapture();

// initialize the face tracker

faceDetection.InitFaceDetection();

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

finished = captureFrame.CaptureNextFrame();

if (finished)

{

captureFrame.DeallocateFrames();

cvDestroyWindow(windowName);

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

while(1)

{

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

finished = captureFrame.CaptureNextFrame();

if (finished)

{

captureFrame.DeallocateFrames();

cvDestroyWindow(windowName);

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

}

else

{

// count the good values

tempFeatures[numFeatures] = featuresB[i];

numFeatures++;

// 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;

}

else

{

// 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];

cnt++;

}

}

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

expandROIMult *= EXPAND_ROI_INIT;

// 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);

if (dist < ADD_FEATURE_MAX_DIST)

{

features[numFeatures] = additionalFeatures[i];

numFeatures++;

}

}

// 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;

}

}

else

{

// reset the expand roi mult back to the init

// since this frame didn't need an expansion

expandROIMult = EXPAND_ROI_INIT;

}

// find the new track box

if (!died)

featureTrackBox = findTrackBox(features, numFeatures);

}

else

{

// 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

}

else

{

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

faceDetectRect = faceDetection.detectFace(currentFrame);

}

}

}

else

{

// 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)

break;

switch(c)

{

case 'r':

numFeatures = 0;

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

faceDetectRect = faceDetection.detectFace(currentFrame);

break;

}

}

// Release the image and tracker

captureFrame.DeallocateFrames();

// Destroy the window previously created

cvDestroyWindow(windowName);

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

cvZero(mask);

// 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

}

else

{

featureCount++;

}

}

//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)

cvReleaseMat(&featureMatrix);

// 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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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

TECH
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

ART
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

Unity

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.

OpenCV

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.

OpenCV

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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