Monitoring Human Activity | |||||||||||||||||||||||||||||||||||
A project of the
Artifical Intelligence, Robotics and Vision Laboratory University of Minnesota, Department of Computer Science and Engineering |
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Tracking People with Probabilistic Occlusion Reasoning Tracking of people in crowded scenes is challenging because pecause people occlude each other when they walk around. The latest revision of our person tracker uses adaptive appearance models that explicitly account for the probability that a person may be partially occluded. All potentially occluding targets are tracked jointly, and the most likely visibility order is estimated (so we know the probability that person A is occluding person B). Target size adaptation is performed using calibration information about the camera, and the reported target positions are in real-world coordinates. Tracking People Using Pan-Tilt-Zoom Cameras
Tracking People in Simultaneous Video Streams
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