Object Tracking via Discriminative Anisotropic Propagation

Anisotropic propagations have been widely used for image processing for decades. However, most previous anisotropic propagations are simply defined on regular image pixels and cannot be used for complex vision task, e.g., object tracking. Visual tracking is a fundamental task in computer vision. In this paper, we proposed a novel discriminative anisotropic propagations model called sequential heat diffusions (SHD) on video sequences to address this issue. Our core idea is to propagate the discriminative appearance of the target object on both the temporal and spatial domains. In particular, we first train a discriminative appearance model for the target. Then for a coming frame, we design two coupled diffusions, in which the spatial one estimates a probability to reflect the intrinsic object structure and the temporal one also provides another probability (guided by information of training frames) to capture the background distribution. Finally, the tracking result is achieved by maximizing a combined confidence maps. The experiments on many challenging videos show the superiority of our method against other state-of-the-art trackers.