TY - GEN
T1 - GroupTrack
T2 - 12th International Conference on Control, Decision and Information Technologies, CoDIT 2026
AU - Zhao, Minjin
AU - Cui, Luheng
AU - Guo, Shupeng
AU - Liu, Chujun
AU - Jiang, Yuxuan
AU - Ding, Yan
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - Group target tracking in sequential images is crucial for applications like public security. This paper proposes GroupTrack, a novel group multi-object tracking method based on temporal awareness and multi-modal association. The framework comprises a Feature-aware Enhanced Group Detection Network (FEGDN) and a Multi-Dimensional Similarity-based association strategy (MDS). FEGDN enhances robustness through multi-scale perception and bidirectional feature fusion, while MDS achieves stable trajectory association by fusing appearance, IoU, and scale consistency. Experiments on the dataset from the "National Big Data and Computational Intelligence Challenge"show that GroupTrack achieves a state-of-the-art average score of 88.18%, significantly outperforming advanced baselines. It secured the first prize in the competition, demonstrating its advancement and practical utility.
AB - Group target tracking in sequential images is crucial for applications like public security. This paper proposes GroupTrack, a novel group multi-object tracking method based on temporal awareness and multi-modal association. The framework comprises a Feature-aware Enhanced Group Detection Network (FEGDN) and a Multi-Dimensional Similarity-based association strategy (MDS). FEGDN enhances robustness through multi-scale perception and bidirectional feature fusion, while MDS achieves stable trajectory association by fusing appearance, IoU, and scale consistency. Experiments on the dataset from the "National Big Data and Computational Intelligence Challenge"show that GroupTrack achieves a state-of-the-art average score of 88.18%, significantly outperforming advanced baselines. It secured the first prize in the competition, demonstrating its advancement and practical utility.
KW - Feature-aware Enhancement
KW - Group Multi-Object Tracking
KW - Multi-Dimensional Similarity
KW - Sequential Images
KW - Trajectory Association
UR - https://www.scopus.com/pages/publications/105047834512
U2 - 10.1109/CoDIT70676.2026.11630797
DO - 10.1109/CoDIT70676.2026.11630797
M3 - Conference contribution
AN - SCOPUS:105047834512
T3 - 12th 2026 International Conference on Control, Decision and Information Technologies, CoDIT 2026
SP - 1208
EP - 1213
BT - 12th 2026 International Conference on Control, Decision and Information Technologies, CoDIT 2026
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 July 2026 through 16 July 2026
ER -