1. Presentation
Zach Robertson

2. 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

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

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

5. 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