A Graphical Social Topology Model for Multi-Object Tracking
Shan Gao, Xiaogang Chen, Qixiang Ye, Junliang Xing, Arjan Kuijper,, Xiangyang Ji

TL;DR
This paper introduces a Graphical Social Topology (GST) model for multi-object tracking that captures group dynamics and object relationships through a topological representation, improving tracking accuracy in crowded scenes.
Contribution
The paper proposes a novel GST model that jointly models group structure and object states, enabling dynamic group analysis and better occlusion handling in multi-object tracking.
Findings
Improved tracking accuracy in crowded scenes.
Effective modeling of group birth, death, merging, and splitting.
Enhanced occlusion handling by considering group cohesion.
Abstract
Tracking multiple objects is a challenging task when objects move in groups and occlude each other. Existing methods have investigated the problems of group division and group energy-minimization; however, lacking overall object-group topology modeling limits their ability in handling complex object and group dynamics. Inspired with the social affinity property of moving objects, we propose a Graphical Social Topology (GST) model, which estimates the group dynamics by jointly modeling the group structure and the states of objects using a topological representation. With such topology representation, moving objects are not only assigned to groups, but also dynamically connected with each other, which enables in-group individuals to be correctly associated and the cohesion of each group to be precisely modeled. Using well-designed topology learning modules and topology training, we infer…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Pose and Action Recognition · Advanced Vision and Imaging
