Presentation Zach Robertson

What did we do last week? Experimenting with different thresholds Dilating the aligned masks on images Understanding the Pedro training process Worked with his code Finding where HOG features are calculated in code for replacement Setting up the Kinect new SDK and testing its performance for human detection It failed

Our method Pedro’s method Low threshold, false positive Low threshold, no false positive High threshold, false negative

Problems Segmenting causes floor to be attached to person, leads to false positives Segmenting sometimes produces an outline of human, leads to false positives

Steps for 3d Human Modal Training Cross product of HOG features of depth with each section of RGB image Get a 3d vector for each pixel describing depth orientation and RGB gradient Place each vector in orientation bins based on the following formula (thetaStep – 1)*(number of phi bins) + phiStep Train based on this