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In currently available literature, no tracking-by-detection (TBD)paradigm-based tracking method has considered the localization confidence ofdetection boxes. In most TBD-based methods, it is considered that objects oflow detection confidence are highly occluded and thus it is a normal practiceto directly disregard such objects or to reduce their priority in matching. Inaddition, appearance similarity is not a factor to consider for matching theseobjects. However, in terms of the detection confidence fusing classificationand localization, objects of low detection confidence may have inaccuratelocalization but clear appearance; similarly, objects of high detectionconfidence may have inaccurate localization or unclear appearance; yet theseobjects are not further classified. In view of these issues, we proposeLocalization-Guided Track (LG-Track). Firstly, localization confidence isapplied in MOT for the first time, with appearance clarity and localizationaccuracy of detection boxes taken into account, and an effective deepassociation mechanism is designed; secondly, based on the classificationconfidence and localization confidence, a more appropriate cost matrix can beselected and used; finally, extensive experiments have been conducted on MOT17and MOT20 datasets. The results show that our proposed method outperforms thecompared state-of-art tracking methods. For the benefit of the community, ourcode has been made publicly at https://github.com/mengting2023/LG-Track.

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