CIOMP OpenIR  > 中科院长春光机所知识产出
Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion
Zhou, T. X.; M. Zhu; D. Zeng and H. Yang
2017
Source PublicationMathematical Problems in Engineering
AbstractVisual 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.
Indexed Bysci ; ei
Language英语
Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/59500
Collection中科院长春光机所知识产出
Recommended Citation
GB/T 7714
Zhou, T. X.,M. Zhu,D. Zeng and H. Yang. Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion[J]. Mathematical Problems in Engineering,2017.
APA Zhou, T. X.,M. Zhu,&D. Zeng and H. Yang.(2017).Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion.Mathematical Problems in Engineering.
MLA Zhou, T. X.,et al."Scale Adaptive Kernelized Correlation Filter Tracker with Feature Fusion".Mathematical Problems in Engineering (2017).
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