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Continuous multi-views tracking using tensor voting

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3 Author(s)
Jinman Kang ; Inst. for Robotics & Intelligent Syst., Univ. of Southern California, Los Angeles, CA, USA ; Cohen, I. ; Medioni, G.

The paper presents a new approach for continuous tracking of moving objects observed by multiple fixed cameras. The continuous tracking of moving objects in each view is realized using a tensor voting based approach. We infer objects' trajectories by performing a perceptual grouping in 2D+t using tensor voting. Also, a multi-scale approach to bridge gaps in object trajectories is presented The trajectories obtained from the multiple cameras are registered in space and time, allowing a synchronization of the video streams and a continuous tracking of objects across multiple views. We demonstrate the performance of the system on several real video surveillance sequences.

Published in:

Motion and Video Computing, 2002. Proceedings. Workshop on

Date of Conference:

5-6 Dec. 2002

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