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Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation
Gao, Shan1; Ye, Qixiang2; Xing, Junliang3; Kuijper, Arjan4; Han, Zhenjun2; Jiao, Jianbin2; Ji, Xiangyang1
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2017-12-01
Volume26Issue:12Pages:5575-5589
SubtypeArticle
AbstractTracking 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.
KeywordMultiple Person Tracking Group Tracking Rgb-d Data Topology
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2017.2708901
WOS KeywordMULTITARGET TRACKING ; MULTIOBJECT TRACKING ; MULTIPERSON TRACKING ; PEDESTRIAN DETECTION ; DATA ASSOCIATION ; CRF MODEL ; MOTION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61325003 ; China Post-doctoral Science Foundation(2016M601028) ; 61620106005 ; 61601466 ; 61672519)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000409526000002
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20031
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.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
Recommended Citation
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.
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