Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections

• URL: http://arxiv.org/abs/2309.09765v1
• Date: Mon, 18 Sep 2023 13:45:35 GMT
• Title: Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections
• Authors: Ting Meng, Chunyun Fu, Mingguang Huang, Xiyang Wang, Jiawei He, Tao Huang, Wankai Shi
• Abstract summary: localization confidence is applied in MOT for the first time, with appearance clarity and localization accuracy of detection boxes taken into account. Our proposed method outperforms the compared state-of-art tracking methods.
• Score: 4.565826090373598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In currently available literature, no tracking-by-detection (TBD) paradigm-based tracking method has considered the localization confidence of detection boxes. In most TBD-based methods, it is considered that objects of low detection confidence are highly occluded and thus it is a normal practice to directly disregard such objects or to reduce their priority in matching. In addition, appearance similarity is not a factor to consider for matching these objects. However, in terms of the detection confidence fusing classification and localization, objects of low detection confidence may have inaccurate localization but clear appearance; similarly, objects of high detection confidence may have inaccurate localization or unclear appearance; yet these objects are not further classified. In view of these issues, we propose Localization-Guided Track (LG-Track). Firstly, localization confidence is applied in MOT for the first time, with appearance clarity and localization accuracy of detection boxes taken into account, and an effective deep association mechanism is designed; secondly, based on the classification confidence and localization confidence, a more appropriate cost matrix can be selected and used; finally, extensive experiments have been conducted on MOT17 and MOT20 datasets. The results show that our proposed method outperforms the compared state-of-art tracking methods. For the benefit of the community, our code has been made publicly at https://github.com/mengting2023/LG-Track.

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