Robust Visual Tracking Using the Time-Reversibility Constraint

Abstract

Visual tracking is a very important front-end to many vision applications. We present a new framework for robust visual tracking in this paper. Instead of just looking forward in the time domain, we incorporate both forward and backward processing of video frames using a novel time-reversibility constraint. This leads to a new minimization criterion that combines the forward and backward similarity functions and the distances of the state vectors between the forward and backward states of the tracker. The new framework reduces the possibility of the tracker getting stuck in local minima and significantly improves the tracking robustness and accuracy. Our approach is general enough to be incorporated into most of the current tracking algorithms. We illustrate the improvements due to the proposed approach for the popular KLT tracker and a search based tracker. The experimental results show that the improved KLT tracker significantly outperforms the original KLT tracker. The time-reversibility constraint used for tracking can be incorporated to improve the performance of optical flow, mean shift tracking and other algorithms.