1. Mentor: Salman Khokhar
Human Detection
Mentor: Salman Khokhar

2. Preview Compute human features with low thresholding
DPM head and shoulders
DPM full body
Compute Optical Flow
threshold
logical mask
Compute Foreground/Background Separation
gaussian mixture model
personal code
False Positive Suppression
optical flow
foreground detection
Improvements & Future Work

3. Compute DPM Detection Use head and shoulders detections
oversampling → many false positives
encompass occluded people
Compare to full body DPM detections

4. Compute Optical Flow Temporal Information localize motion
identify humans performing action
goal: action recognition
Thresholding
determine level
logical mask

5. OpFlow Results

6. Compute Foreground Separate Scenery from Objects of Interest
Foreground detection produces mask
requires stabilized images
Methods Used
gaussian mixture models
pixel represented by probability density
account for small noise

7. FrGnd GMM Results

8. False Positive Suppression
Eliminate extra detections
pixel motion
background/scenery
GMM Foreground Model Used
Detection eliminated if falls below threshold
optical flow & foreground
detection window subsamples

9. FsePosSupp (Head) Results

10. FsePosSupp (Body) Results

11. Future Work/Improvements
Use Haroon’s Code in Place of DPM
Devise own human detection
Possible tracking?
Semantic Segmentation