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Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation

文献类型：期刊论文

作者Gao, Shan1; Ye, Qixiang2; Xing, Junliang3; Kuijper, Arjan4; Han, Zhenjun2; Jiao, Jianbin2; Ji, Xiangyang1
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2017-12-01
卷号26期号:12页码:5575-5589
关键词Multiple Person Tracking Group Tracking Rgb-d Data Topology
DOI10.1109/TIP.2017.2708901
文献子类Article
英文摘要Tracking multiple persons is a challenging task when persons move in groups and occlude each other. Existing group-based methods have extensively investigated how to make group division more accurately in a tracking-by-detection framework; however, few of them quantify the group dynamics from the perspective of targets' spatial topology or consider the group in a dynamic view. Inspired by the sociological properties of pedestrians, we propose a novel socio-topology model with a topology-energy function to factor the group dynamics of moving persons and groups. In this model, minimizing the topology-energy-variance in a two-level energy form is expected to produce smooth topology transitions, stable group tracking, and accurate target association. To search for the strong minimum in energy variation, we design the discrete group-tracklet jump moves embedded in the gradient descent method, which ensures that the moves reduce the energy variation of group and trajectory alternately in the varying topology dimension. Experimental results on both RGB and RGB-D data sets show the superiority of our proposed model for multiple person tracking in crowd scenes.
WOS关键词MULTITARGET TRACKING ; MULTIOBJECT TRACKING ; MULTIPERSON TRACKING ; PEDESTRIAN DETECTION ; DATA ASSOCIATION ; CRF MODEL ; MOTION
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000409526000002
资助机构National Natural Science Foundation of China(61325003 ; China Post-doctoral Science Foundation(2016M601028) ; 61620106005 ; 61601466 ; 61672519)
源URL[http://ir.ia.ac.cn/handle/173211/20031]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
作者单位1.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
3.Chinese Acad Sci CASIA, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Technol Univ Darmstadt, Fraunhofer Inst Comp Graph Res, D-64283 Darmstadt, Germany
推荐引用方式
GB/T 7714		 Gao, Shan,Ye, Qixiang,Xing, Junliang,et al. Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(12):5575-5589.
APA Gao, Shan.,Ye, Qixiang.,Xing, Junliang.,Kuijper, Arjan.,Han, Zhenjun.,...&Ji, Xiangyang.(2017).Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(12),5575-5589.
MLA Gao, Shan,et al."Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.12(2017):5575-5589.

入库方式： OAI收割

来源：自动化研究所

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