Learning the Behavior of Users in a Public Space through Video Tracking
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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.
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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
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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
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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
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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
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Results
• Counts– 26 human– 32 computer
• Time of stay by the fountainmanual difficult
On the 6m (10fps) dataset
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On the 6m (10fps) dataset
Walking path
Wondering people
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Large-scale results• Without human evaluation• Total number of people entered the plaza• Total number of people who sat
~5% ~1% ~0.4%
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• Probability that a person chose to sit by the fountain depending on the number of people already sitting there.
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• Distribution of time of stay
– More
– Longer
Secondary seating is more popular than primary seating
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Walking pathWondering path
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Simulations
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Discussions
• Very clear problem statements• Validate the system on a small data set before
applying it to bigger ones