Continuous Multi-Views Tracking using Tensor Voting

Jinman Kang


This 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 bridging 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.

Maintained by Philippos Mordohai