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

Gao, Shan and Ye, Qixiang and Xing, Junliang and Kuijper, Arjan and Han, zhenjun and Jiao, Jianbin and Ji, Xiangyang (2017):
Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation.
In: IEEE Transactions on Image Processing, 26 (12), pp. 5575-5589. ISSN 1057-7149,
DOI: 10.1109/TIP.2017.2708901,
[Article]

Abstract

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 topologyenergy- 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.

Item Type: Article
Erschienen: 2017
Creators: Gao, Shan and Ye, Qixiang and Xing, Junliang and Kuijper, Arjan and Han, zhenjun and Jiao, Jianbin and Ji, Xiangyang
Title: Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation
Language: English
Abstract:

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 topologyenergy- 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.

Journal or Publication Title: IEEE Transactions on Image Processing
Journal volume: 26
Number: 12
Uncontrolled Keywords: People tracking, Topology, RGB color space
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 04 May 2020 08:45
DOI: 10.1109/TIP.2017.2708901
Official URL: https://doi.org/10.1109/TIP.2017.2708901
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