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Mathematical Problems in Engineering
Volume 2017, Article ID 1605959, 8 pages
Research Article

Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion

1Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun 130033, China
2The University of the Chinese Academy of Sciences, Beijing 100049, China
3The Key Laboratory of Airborne Optical Imaging and Measurement, Chinese Academy of Sciences, Changchun 130033, China

Correspondence should be addressed to Tongxue Zhou; moc.361@2991euxgnotuohz

Received 13 May 2017; Revised 26 June 2017; Accepted 6 July 2017; Published 17 October 2017

Academic Editor: Mauro Gaggero

Copyright © 2017 Tongxue Zhou 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.


Visual tracking is one of the most important components in numerous applications of computer vision. Although correlation filter based trackers gained popularity due to their efficiency, there is a need to improve the overall tracking capability. In this paper, a tracking algorithm based on the kernelized correlation filter (KCF) is proposed. First, fused features including HOG, color-naming, and HSV are employed to boost the tracking performance. Second, to tackle the fixed template size, a scale adaptive scheme is proposed which strengthens the tracking precision. Third, an adaptive learning rate and an occlusion detection mechanism are presented to update the target appearance model in presence of occlusion problem. Extensive evaluation on the OTB-2013 dataset demonstrates that the proposed tracker outperforms the state-of-the-art trackers significantly. The results show that our tracker gets a 14.79% improvement in success rate and a 7.43% improvement in precision rate compared to the original KCF tracker, and our tracker is robust to illumination variations, scale variations, occlusion, and other complex scenes.