Localization-Guided Track: A Deep Association Multi-Object Tracking
  Framework Based on Localization Confidence of Detections
Ting Meng Chunyun Fu Mingguang Huang Xiyang Wang Jiawei He Tao Huang Wankai Shi
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.