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Mathematical Problems in Engineering
Volume 2017, Article ID 3276103, 10 pages
https://doi.org/10.1155/2017/3276103
Research Article

Robust Visual Tracking Using the Bidirectional Scale Estimation

1Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 201203, China
2Key Laboratory of Intelligent Information Processing in Universities of Shandong, Shandong Institute of Business and Technology, Yantai 264005, China

Correspondence should be addressed to An Zhiyong; moc.361@tuytyza

Received 21 August 2016; Accepted 18 December 2016; Published 19 January 2017

Academic Editor: Francisco Valero

Copyright © 2017 An Zhiyong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.