Object Tracking in Low-Frame-Rate Video

In this paper, we present an object detection and tracking algorithm for low-frame-rate applications. We extend the standard mean-shift technique such that is is not limited within a single kernel but uses multiple kernels centered around high motion areas obtained by change detection. We also improve the convergence properties of the mean-shift by integrating two additional likelihood terms using object templates. Our simulations prove the effectiveness of the proposed method both under heavy occlusions and low frame rates down to 1-fps. SPIE Image and Video Communications and Processing This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2005 201 Broadway, Cambridge, Massachusetts 02139