Tracking Segmented Objects Using Tensor Voting

Abstract

The paper presents a new approach to track objects in motion when observed by a fixed camera, with severe occlusions, merging/splitting objects and defects in the detection. We first detect regions corresponding to moving objects in each frame, then try to establish their trajectory. We propose to implement the temporal continuity constraint efficiently, and apply it to tracking problems in realistic scenarios. The method is based on a spatiotemporal (2D+t) representation of the moving regions, and uses the tensor voting methodology to enforce smoothness in space and table of the tracked objects. Although other characteristics may be considered, only the connected components of the moving regions are used, without further assumptions about the object being tracked. We demonstrate the performance of the system on several real sequences.