TCGM: Spatiotemporal Clues for Multiobject Tracking via Topological Chronology Graph Neural Network
Wenyuan Qin, Heyi Quan, Xiangxi Kong, Hao Xu, Chengwei Pan, Xiwang Dong · IEEE Sensors Journal · 2025
Multi-Object Tracking (MOT) aims to detect and uniquely identify objects within a video sequence. Traditional methods often focus on processing data between consecutive frames or primarily rely on motion cues to represent object states. However, these approaches often overlook the influence of historical data on the association process. To address this gap, this paper introduces a Topological Chronology Graph Model (TCGM) designed to bridge the historical and current states of objects. The TCGM consists of two main components: the Topological Chronology Graph Builder (TCGB) and the Spatio-Temporal Feature Integrator (STFI). TCGB encodes historical trajectory data and establishes associative links with newly detected objects to model spatio-temporal topological relationships. Meanwhile, the STFI integrates current and historical data through a spatio-temporal transformer to comprehensively represent the state of each object. Our algorithm has been rigorously evaluated on the MOT17, MOT20, DanceTrack and KITTI benchmarks, achieving competitive results.