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A Graphical Social Topology Model for RGB-D Multi-Person Tracking

Gao, Shan ; Ye, Qixiang ; Liu, Li ; Kuijper, Arjan ; Ji, Xiangyang (2021):
A Graphical Social Topology Model for RGB-D Multi-Person Tracking.
In: Transactions on Circuits and Systems for Video Technology, (Early Access), p. 15. IEEE, ISSN 1051-8215,
DOI: 10.1109/TCSVT.2021.3049397,
[Article]

Abstract

Tracking multiple persons is a challenging task especially when persons move in groups and occlude one another. Existing research have investigated the problems of group division and segmentation; however, lacking overall person-group topology modeling limits the ability to handle complex person and group dynamics. We propose a Graphical Social Topology (GST) model in the RGB-D data domain, and estimate object group dynamics by jointly modeling the group structure and states of persons using RGB-D topological representation. With our topology representation, moving persons are not only assigned to groups, but also dynamically connected with each other, which enables in-group individuals to be correctively associated and the cohesion of each group to be precisely modeled. Using the learned typical topology pattern and group online update modules, we infer the birth/death and merging/splitting of dynamic groups. With the GST model, the proposed multi-person tracker can naturally facilitate the occlusion problem by treating the occluded object and other in-group members as a whole, while leveraging overall state transition. Experiments on different RGB-D and RGB datasets confirm that the proposed multi-person tracker improves the state-of-the-arts.

Item Type: Article
Erschienen: 2021
Creators: Gao, Shan ; Ye, Qixiang ; Liu, Li ; Kuijper, Arjan ; Ji, Xiangyang
Title: A Graphical Social Topology Model for RGB-D Multi-Person Tracking
Language: English
Abstract:

Tracking multiple persons is a challenging task especially when persons move in groups and occlude one another. Existing research have investigated the problems of group division and segmentation; however, lacking overall person-group topology modeling limits the ability to handle complex person and group dynamics. We propose a Graphical Social Topology (GST) model in the RGB-D data domain, and estimate object group dynamics by jointly modeling the group structure and states of persons using RGB-D topological representation. With our topology representation, moving persons are not only assigned to groups, but also dynamically connected with each other, which enables in-group individuals to be correctively associated and the cohesion of each group to be precisely modeled. Using the learned typical topology pattern and group online update modules, we infer the birth/death and merging/splitting of dynamic groups. With the GST model, the proposed multi-person tracker can naturally facilitate the occlusion problem by treating the occluded object and other in-group members as a whole, while leveraging overall state transition. Experiments on different RGB-D and RGB datasets confirm that the proposed multi-person tracker improves the state-of-the-arts.

Journal or Publication Title: Transactions on Circuits and Systems for Video Technology
Number: Early Access
Publisher: IEEE
Uncontrolled Keywords: Topology, Feature extraction, Data models, Model-based tracking, Object tracking, People tracking, Network topologies
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 25 Jan 2021 11:29
DOI: 10.1109/TCSVT.2021.3049397
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