Robust Visual Tracking Using the Time-Reversibility Constraint

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 timereversibility 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-reversib...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Hao Wu, Rama Chellappa, Aswin C. Sankaranarayanan, Shaohua Kevin Zhou
• Keywords: Computer Vision, ICCV 2007, KLT Tracker, Mean Shift Tracking, Original Klt Tracker, Popular Klt Tracker, Robust Visual Tracking