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.

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

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

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

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

Results

• Counts– 26 human– 32 computer

• Time of stay by the fountainmanual difficult

On the 6m (10fps) dataset

On the 6m (10fps) dataset

Walking path

Wondering people

Large-scale results• Without human evaluation• Total number of people entered the plaza• Total number of people who sat

~5% ~1% ~0.4%

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

• Distribution of time of stay

– More

– Longer

Secondary seating is more popular than primary seating

Walking pathWondering path

Simulations

Discussions

• Very clear problem statements• Validate the system on a small data set before

applying it to bigger ones