Multi-object tracking algorithm based on interactive attention network and adaptive trajectory reconnection

Multi-object tracking (MOT) detects multiple targets in an image and assigns a unique identifier to each target. However, challenges such as rapid motion, occlusion, and camera motion in the tracking scene may lead to identity switches (IDs) and missing trajectory problems, which degrade the perform...

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Vydáno v:Expert systems with applications Ročník 249; s. 123581
Hlavní autoři: Ma, Sugang, Duan, Shuaipeng, Hou, Zhiqiang, Yu, Wangsheng, Pu, Lei, Zhao, Xiangmo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2024
Témata:
Adaptive trajectory reconnection
Camera motion compensation
Cost matrix
Interactive attention network
Multi-object tracking
Interactive attention network
Adaptive trajectory reconnection
Camera motion compensation
Multi-object tracking
Cost matrix
ISSN:0957-4174, 1873-6793
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Shrnutí:Multi-object tracking (MOT) detects multiple targets in an image and assigns a unique identifier to each target. However, challenges such as rapid motion, occlusion, and camera motion in the tracking scene may lead to identity switches (IDs) and missing trajectory problems, which degrade the performance of the tracker. To address these issues, this paper presents an MOT algorithm based on an interactive attention network and adaptive trajectory reconnection. First, an interactive attention network was created to learn the features for two different tasks of detection and tracking to alleviate feature conflicts in order to extract sufficient feature information. A new cost matrix was then designed to fuse the motion and feature information, thereby reducing the number of IDs. Meanwhile, the extreme gradient boosting reconnection module was used to achieve adaptive trajectory reconnection and reduce missing trajectories. The proposed algorithm achieved 61.5% and 55.4% HOTA using the standard MOT17 and MOT20 datasets, respectively. In comparison to FairMOT, our algorithm showcased notable enhancements of 3% and 1.6% on these datasets. Furthermore, when compared to state-of-the-art algorithms, the proposed algorithm demonstrated superior tracking performance.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2024.123581