Real-time compressive tracking with motion estimation
Jiayun Wu, Daquan Chen, Yi Rui · 2013
Visual tracking is challenging due to appearance changes caused by motion, illumination, occlusion and pose, among others. For these local changes, appearance model based tracking algorithms, such as MILtracker [8], have adopted local features and most recently extended to compressive domain, namely Compressive Tracking [13], for the real-time performance. However, the motion information is missed out from these trackers and assumptions on target motion have been made by predefined search radii. In this paper, the motion information has been integrated into appearance model based tracking by introducing motion estimator, i.e., particle filters. The experiments show that motion estimator could improve the performance of appearance based trackers especially when the target is with motion variety.