Continuous Multi-Views Tracking using Tensor Voting
Abstract
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.