1. Counting in High-Density Crowd Videos
Edgar Lopez
Mentor: Dr. Haroon Idrees

2. Video Annotations Method Compute forward tracks.
Compute backward tracks.
Match forward and backward tracks.
Distance
Appearance (Intensity Histogram)
Added the constraint that if distance is large keep both tracks and don’t match.
Compute average tracks by using weighting system.
t frame
t+step frame

3. Video Annotations
Manually Annotated Frame

4. Video Annotations
Annotations computed by our method.

5. Video Annotations
Manually Annotated Frame

6. Motion for Counting and Segmentation
Saad Ali and Mubarak Shah, A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, June 19-21, 2007.

7. Crowd Flow Segmentation
Segmentation using Saad Ali’s method.
Input (Video)
Output (1 Segmentation Mask)

8. Density Segmentation Segmentation using our method. Input (1 Frame)
Output (1 Segmentation Mask)

9. Crowd Segmentation Challenges with this Video Low Resolution
Saad Ali’s Method
Our Method
Challenges with this Video
Low Resolution
Very noisy
Hard to compute segmentation using density map.

10. Counting in Videos Compute superpixels for each frame.
Compute counts for each superpixel in all frames.
Compute trajectories.
Use trajectories to construct a Conditional Random Field (CRF) to smooth/improve counts between frames.