1. Learning the Behavior of Users in a Public Space through Video Tracking
Yan, W. and Forsyth, D. "Learning the Behavior of Users in a Public Space through Video Tracking", in Proceedings of IEEE Workshop on Applications of Computer Vision (WACV) , 2005
Yan, W. and Kalay, Y.E. "Simulating the Behavior of Users in Built Environments", in Journal of Architectural and Planning Research (JAPR) 21:4, winter 2004.

3. Problem Statement
Analyze mass data of human behavior in a public space
Input: 8 hours of video in Sproul Plaza
3pm to 5pm for 4 days
human observers to provide validation
Output: statistical measurements that can be used to evaluate architecture design in terms of human behavior

4. The Tracking System Head detector
Background model: averaging frames manually selected
Intensity thresholding: assume dark head/upper body
ROI
Background Subtraction
Intensity Thresholding
Blob Merging

5. The Tracking System Tracking by data association
Spatial proximity (sitting) and consistency in velocity (walking)
Hungarian algorithm to link blobs from frame to frame a

6. Shadow
Using geometric context to avoid the human blobs to be linked by cast shadows
Compute the location of the feet
Cut off the lower 2/3 of the blob

7. Results Counts Time of stay by the fountain 26 human 32 computer
manual difficult
On the 6m (10fps) dataset

8. Walking path
Wondering people
On the 6m (10fps) dataset

9. Large-scale results Without human evaluation
Total number of people entered the plaza
Total number of people who sat
~5%
~1%
~0.4%

10. Probability that a person chose to sit by the fountain depending on the number of people already sitting there.

11. Distribution of time of stay
More
Longer
Secondary seating is more popular than primary seating

12. Walking path
Wondering path

13. Simulations

15. Discussions Very clear problem statements
Validate the system on a small data set before applying it to bigger ones