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Visual Tracking Based on Multi-Feature and Fast Scale Adaptive Kernelized Correlation Filter

文献类型：期刊论文

作者Zeng, Xianyou1,2; Xu, Long3; Cen, Yigang1,2; Zhao, Ruizhen1,2; Hu, Shaohai1,2; Xiao, Guohui4
刊名IEEE ACCESS
出版日期2019
卷号7页码:83209-83228
关键词Visual tracking scale filter dimension reduction multiple feature fusion dynamic learning rate
ISSN号2169-3536
DOI10.1109/ACCESS.2019.2924746
英文摘要Tracking methods based on a correlation filter have attracted much attention because of their high efficiency and strong robustness. However, a tracker based on a single feature is obviously not sufficient to adapt to the complex appearance changes of the target. Besides, rapid and exact scale estimation is still a challenging problem in the field of visual tracking. In this paper, we introduce an independent scale filter for the estimation of the scale of an object and merge two complementary features to further boost the performance of the tracker. At the same time, a dimension reduction strategy is adopted to decrease the computational load. Finally, a dynamic learning rate-based model update mechanism is inserted to effectively alleviate model degradation problem by suppressing the influence of noisy appearance changes. The extensive experiments were conducted on the object tracking benchmark (OTB) dataset and Temple color 128 dataset. The quantitative and qualitative results exhibit that compared with other popular trackers, the tracker proposed in this paper acquires favorable results in tracking accuracy, efficiency, and robustness. On the OTB-2015 benchmark dataset, it obtains precision scores of 0.773, 0.782, and 0.714 and success scores of 0.585, 0.606, and 0.534 in the three indexes of OPE, TRE, and SRE. On the Temple color 128 dataset, it acquires precision scores of 0.641, 0.681, and 0.606 and success scores of 0.478, 0.515, and 0.445 in the three indexes of OPE, TRE, and SRE, surpassing many well-known tracking methods. In terms of tracking efficiency, it runs at a speed of 42.3 frames/s on a single CPU, making it suitable for real-time applications.
WOS关键词OBJECT TRACKING
资助项目National Natural Science Foundation of China (NSFC)[61872034] ; National Natural Science Foundation of China (NSFC)[61572067] ; National Natural Science Foundation of China (NSFC)[61572461] ; National Natural Science Foundation of China (NSFC)[11790305] ; National Natural Science Foundation of China (NSFC)[11433006] ; National Natural Science Foundation of China (NSFC)[61572063] ; National Natural Science Foundation of China (NSFC)[61841503] ; National Natural Science Foundation of China (NSFC)[61741507]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000475474100001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC)
源URL[http://ir.bao.ac.cn/handle/114a11/26667]  
专题中国科学院国家天文台
通讯作者Zhao, Ruizhen
作者单位1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
2.Beijing Jiaotong Univ, Key Lab Adv Informat Sci & Network Technol, Beijing 100044, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100012, Peoples R China
4.Jiangxi Sci & Technol Normal Univ, Jiangxi Prov Key Lab Optoelect & Commun, Nanchang 330038, Jiangxi, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Xianyou,Xu, Long,Cen, Yigang,et al. Visual Tracking Based on Multi-Feature and Fast Scale Adaptive Kernelized Correlation Filter[J]. IEEE ACCESS,2019,7:83209-83228.
APA Zeng, Xianyou,Xu, Long,Cen, Yigang,Zhao, Ruizhen,Hu, Shaohai,&Xiao, Guohui.(2019).Visual Tracking Based on Multi-Feature and Fast Scale Adaptive Kernelized Correlation Filter.IEEE ACCESS,7,83209-83228.
MLA Zeng, Xianyou,et al."Visual Tracking Based on Multi-Feature and Fast Scale Adaptive Kernelized Correlation Filter".IEEE ACCESS 7(2019):83209-83228.

入库方式： OAI收割

来源：国家天文台

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