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Visual tracking using global sparse coding and local convolutional features

文献类型：期刊论文

作者Zeng, Xianyou3,4; Xu, Long2; Ma, Lin1; Zhao, Ruizhen3,4; Cen, Yigang3,4
刊名DIGITAL SIGNAL PROCESSING
出版日期2018
卷号72页码:115-125
关键词Visual tracking Sparse representation Local convolutional feature Collaborative model
ISSN号1051-2004
DOI10.1016/j.dsp.2017.10.007
英文摘要Visual tracking is a challenging task in many computer vision applications due to factors such as occlusion, scale variations, background clutter, and so on. In this paper, we present a robust tracking algorithm by representing the target at two levels: global and local levels. Accordingly, the tracking algorithm is composed of two parts: global and local parts. The global part is a discriminative model which separates the foreground object from the background based on holistic features. In the local part, we explore the target's local representation by a set of filters convolving the target region at each position. Then, the global part and local part are integrated into a collaborative model to construct the final tracker. Experiments on the tracking benchmark dataset with 50 challenging videos demonstrate the robustness and effectiveness of the proposed algorithm, outperforming several state-of-the-art models. (C) 2017 Elsevier Inc. All rights reserved.
WOS关键词OBJECT TRACKING ; APPEARANCE MODEL ; NEURAL-NETWORKS
资助项目National Natural Science Foundation of China[61572461] ; National Natural Science Foundation of China[61472257] ; National Natural Science Foundation of China[61572067] ; National Natural Science Foundation of China[11433006]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000417776000009
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
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
源URL[http://ir.bao.ac.cn/handle/114a11/37347]  
专题中国科学院国家天文台
通讯作者Xu, Long
作者单位1.Tencent AI Lab, Shenzhen 518052, Peoples R China
2.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100012, Peoples R China
3.Key Lab Adv Informat Sci & Network Technol Beijin, Beijing, Peoples R China
4.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Xianyou,Xu, Long,Ma, Lin,et al. Visual tracking using global sparse coding and local convolutional features[J]. DIGITAL SIGNAL PROCESSING,2018,72:115-125.
APA Zeng, Xianyou,Xu, Long,Ma, Lin,Zhao, Ruizhen,&Cen, Yigang.(2018).Visual tracking using global sparse coding and local convolutional features.DIGITAL SIGNAL PROCESSING,72,115-125.
MLA Zeng, Xianyou,et al."Visual tracking using global sparse coding and local convolutional features".DIGITAL SIGNAL PROCESSING 72(2018):115-125.

入库方式： OAI收割

来源：国家天文台

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