TY - GEN
T1 - Multi-object Tracking with Graph-Aided Structure Correction and Motion Prediction
AU - Liu, Peiqi
AU - Li, Wenling
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Multi-Object-Tracking (MOT) methods have made great efforts on discovering more differences among objects. However, the correlation between objects is rarely discussed which is also important. In this paper, we propose two modules considering their correlation in graph to enhance association accuracy and help motion prediction for MOT methods with Tracking-By-Detection (TBD) paradigm, where the relative positions between objects are represented as graph edges. By ensuring the structure of graph in continuous tracking and regarding the objects in occlusion as a whole, these two modules correct the wrong assignments and predict motions for occluded objects aided by their correlated objects. After inserting our modules into an existing TBD method, the improvements in benchmark results and ablation study of three MOT datasets demonstrate their effectiveness.
AB - Multi-Object-Tracking (MOT) methods have made great efforts on discovering more differences among objects. However, the correlation between objects is rarely discussed which is also important. In this paper, we propose two modules considering their correlation in graph to enhance association accuracy and help motion prediction for MOT methods with Tracking-By-Detection (TBD) paradigm, where the relative positions between objects are represented as graph edges. By ensuring the structure of graph in continuous tracking and regarding the objects in occlusion as a whole, these two modules correct the wrong assignments and predict motions for occluded objects aided by their correlated objects. After inserting our modules into an existing TBD method, the improvements in benchmark results and ablation study of three MOT datasets demonstrate their effectiveness.
KW - Multi-Object Tracking
KW - Object Correlation
KW - Tracking-By-Detection
KW - Visual object tracking
UR - https://www.scopus.com/pages/publications/105000811700
U2 - 10.1007/978-981-96-2240-5_7
DO - 10.1007/978-981-96-2240-5_7
M3 - 会议稿件
AN - SCOPUS:105000811700
SN - 9789819622399
T3 - Lecture Notes in Electrical Engineering
SP - 67
EP - 76
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 11
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -