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.

翻译：在现有文献中，基于检测跟踪（TBD）范式的跟踪方法尚未考虑检测框的定位置信度。大多数TBD方法认为低检测置信度的目标被严重遮挡，因此通常直接忽略此类目标或降低其在匹配中的优先级。此外，外观相似性并非匹配这些目标时需考虑的因素。然而，从检测置信度融合分类与定位的角度看，低检测置信度目标可能定位不准但外观清晰，高检测置信度目标也可能定位不准或外观模糊，但这些目标未被进一步分类。针对这些问题，我们提出定位引导追踪（LG-Track）。首先，首次将定位置信度应用于多目标跟踪，兼顾检测框的外观清晰度与定位准确性，并设计了一种有效的深度关联机制；其次，基于分类置信度与定位置信度，可选取更合适的代价矩阵；最后，在MOT17和MOT20数据集上进行了大量实验。结果表明，我们提出的方法优于对比的最新跟踪方法。为促进社区发展，代码已开源至 https://github.com/mengting2023/LG-Track。