Cited By
View all- Zhang YLiang YElazab AWang ZWang C(2023)Graph Attention Networks and Track Management for Multiple Object TrackingElectronics10.3390/electronics1219407912:19(4079)Online publication date: 28-Sep-2023
DSGA: Distractor-Suppressing Graph Attention for Multi-object Tracking
Pages 69 - 76
Abstract
Multiple object tracking (MOT) methods based on single object tracking are of great interest because of their ability to balance efficiency and performance on the strength of the localization capability of single-target tracking. However, most of the single object tracking methods only distinguish foreground and background. They are susceptible to the influence of similar interfering objects during localization, while in multiple object tracking scenarios, there are more interfering objects and the influence is more severe. Therefore, we propose a Distractor-Suppressing Graph Attention (DSGA) to learn more discriminative attention by reducing the influence of distractors on learning attention weight features. Furthermore, DSGA is embedded into the basic MOT framework “SiamMOT” formed as DSGA-SiamMOT and applied to multiple object tracking to verify its effectiveness. We conduct experiments on the MOT Challenge benchmark with "public detection", and obtain MOTA 66.65%, IDF1 62.2% accuracy on the MOT17 dataset with 14fps.
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Index Terms
- DSGA: Distractor-Suppressing Graph Attention for Multi-object Tracking
Index terms have been assigned to the content through auto-classification.
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November 2022
89 pages
ISBN:9781450397544
DOI:10.1145/3573910
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Published: 20 January 2023
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ICRAI 2022
ICRAI 2022: 2022 8th International Conference on Robotics and Artificial Intelligence
November 18 - 20, 2022
Singapore, Singapore
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Cited By
View all- Zhang YLiang YElazab AWang ZWang C(2023)Graph Attention Networks and Track Management for Multiple Object TrackingElectronics10.3390/electronics1219407912:19(4079)Online publication date: 28-Sep-2023
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