학술논문
Multi-Object Tracking: Decoupling Features to Solve the Contradictory Dilemma of Feature Requirements
Document Type
Periodical
Author
Source
IEEE Transactions on Circuits and Systems for Video Technology IEEE Trans. Circuits Syst. Video Technol. Circuits and Systems for Video Technology, IEEE Transactions on. 33(9):5117-5132 Sep, 2023
Subject
Language
ISSN
1051-8215
1558-2205
1558-2205
Abstract
Multi-object tracking achieves the acquisition of target location information and identity information through two subtasks, detection and re-identification (ReID). The existing commonly used one-shot framework has speed advantages, but the two subtasks have different feature requirements, which leads to competitive learning in the training and thus weakens the feature quality. We propose a feature decoupling based multi-object tracking framework FDTrack for contradictory feature requirements. Through the mutual inhibition of the two subtasks, the features of the backbone network are decoupled. Then the decoupled features are self-constrained to enhance effective features. Considering the instability of the target state and the different confidence of the detections, a more reasonable association strategy is employed to maximize the matchings between detections, thus recovering low-confidence targets. FDTrack is extensively tested on the MOT17 and MOT20 benchmarks. The experimental results show that FDTrack surpasses the previous state-of-the-art (SOTA) methods and has good anti-interference and real-time performance. Moreover, our proposed modules have good portability and can be applied in other one-shot trackers to achieve performance improvement.