Spatial-temporal Context based Multi-people Detection and Tracking
The problem of human detection in video from a distance has been extensively investigated within the computer vision community. It is difficult because of the large variations in human appearance due to changes in illumination, camera position, clothing and body pose, and the motion of the platform (such as ship). To overcome these difficulties, we develop a new approach by incorporating the context information. Specifically, the context of a target in an image sequence has two components: the spatial context including the local background and nearby targets and the temporal context including all appearances of the targets that have been seen previously. A new model for multi-target tracking based on the classification of each target against its spatial context was implemented. The temporal context is included by integrating the entire history of target appearance based on probabilistic principal component analysis (PPCA). A new incremental scheme has been developed that learns the full set of PPCA parameters accurately online. Experimental results demonstrate the superior performance of our approach to the existing techniques in tracking small targets from distance with complex background. Details about this approach may be downloaded from here.
Video demos of this approach may be found here.