Robust tracking with per-exemplar support vector machine

Rongmei Shi; Jun Zhang; Zhao Xie; Jun Gao; Xinxiang Zheng

Robust tracking with per-exemplar support vector machine

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Author(s): Rongmei Shi ¹ ; Jun Zhang ¹ ; Zhao Xie ¹ ; Jun Gao ¹ ; Xinxiang Zheng ¹
- Affiliations: 1: School of Computer and Information, Hefei University of Technology, Hefei, Anhui, 230009People's Republic of China
Source: Volume 9, Issue 5, October 2015, p. 699 – 710
DOI: 10.1049/iet-cvi.2014.0234 , Print ISSN 1751-9632, Online ISSN 1751-9640

Received 28/07/2014, Accepted 12/02/2015, Revised 17/01/2015, Published 20/05/2015

The authors extend exemplar representation to the field of tracking and propose a robust tracking algorithm with per-exemplar support vector machine (SVM) classifiers. First, the authors train the simple yet effective exemplar SVM classifier using the target object as the single positive and mining its surroundings as hard negatives. Second, the authors propose an online ensemble tracker, which integrates the useful ‘key historical templates’ of the target to refine the current template, leading to better discriminative power of tracker and effectively decreasing the risk of drift. Experiments on challenging sequences demonstrate that the tracker performs well in accuracy and robustness, especially under the sequences with strong illumination variation and scale variation, as well as pose change and partial occlusion in the long-time sequence.

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