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Learning the Behavior of Users in a Public Space through Video Tracking

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

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Learning the Behavior of Users in a Public Space through Video Tracking

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  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.

  2. 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

  3. 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

  4. 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

  5. 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

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

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

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

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

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

  11. Walking path Wondering path

  12. Simulations

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

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