Dynamic Channel Pruning For Correlation Filter Based Object Tracking

Abstract

Fusion of multi-channel representations has played a crucial role in the success of correlation filter (CF) based trackers. But, all channels do not contain useful information for target localization at every frame. During challenging scenarios, ambiguous responses of non-discriminative or unreliable channels lead to erroneous results and cause tracker drift. To mitigate this problem, we propose a method for dynamic channel pruning through online (i.e., at every frame) learning of channel weights. Our method uses estimated reliability scores to compute channel weights, to nullify the impact of highly unreliable channels. The proposed method for learning of channel weights is modeled as a non-smooth convex optimization problem. We then propose an algorithm to solve the resulting problem efficiently compared to off-the-shelf solvers. Results on VOT2018 and TC128 datasets show that proposed method improves the performance of baseline CF trackers.