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  4. 中科院合肥智能机械研究所
Support vector correlation filter with long-term tracking

文献类型：期刊论文

作者Wang, Zhongpei1; Wang, Hao1; Fang, Baofu1; Xie, Chengjun2
刊名SIGNAL IMAGE AND VIDEO PROCESSING
出版日期2018-11-01
卷号12期号:8页码:1541-1549
关键词Support correlation filter Long-term tracking Re-detection Passive-aggressive algorithm Max response to average response rate
ISSN号1863-1703
DOI10.1007/s11760-018-1310-0
通讯作者Xie, Chengjun(cjxie@iim.ac.cn)
英文摘要Boosted by the promising advancement of the correlation filter-based tracker, we propose an algorithm called the SLT (support vector correlation filter with long-term tracking) that is based on the new SCF (support vector correlation filter) framework to handle long-term tracking. To perform long-term tracking, we propose using a detector to refine the position that includes occlusion and deformation and is out-of-view. We used a new judgment criterion called the max response to the average response rate (MAR) to activate the re-detection procedure and then exploit the linear support vector machine (SVM) classifier to obtain a positive refinement. Moreover, we do not update the SVM classifier every frame to reduce the number of computations and obtain better samples to improve the accuracy of the classifier. We use the online passive-aggressive learning algorithm for online learning and use the same MAR criterion to active it. Extensive experimental results on the OTB50 benchmark dataset show its superior performance in terms of accuracy and robustness.
WOS关键词OBJECT TRACKING ; ROBUST TRACKING
资助项目National Natural Science Foundation of China[61175033]
WOS研究方向Engineering ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000444312400014
出版者SPRINGER LONDON LTD
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/38837]  
专题合肥物质科学研究院_中科院合肥智能机械研究所
通讯作者Xie, Chengjun
作者单位1.Hefei Univ Technol, Sch Comp & Informat, Hefei 230009, Anhui, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhongpei,Wang, Hao,Fang, Baofu,et al. Support vector correlation filter with long-term tracking[J]. SIGNAL IMAGE AND VIDEO PROCESSING,2018,12(8):1541-1549.
APA Wang, Zhongpei,Wang, Hao,Fang, Baofu,&Xie, Chengjun.(2018).Support vector correlation filter with long-term tracking.SIGNAL IMAGE AND VIDEO PROCESSING,12(8),1541-1549.
MLA Wang, Zhongpei,et al."Support vector correlation filter with long-term tracking".SIGNAL IMAGE AND VIDEO PROCESSING 12.8(2018):1541-1549.

入库方式： OAI收割

来源：合肥物质科学研究院

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