Multiple object tracking (MOT) depends heavily on selection of true positive detected bounding boxes. However, this aspect of the problem is mostly overlooked or mitigated by employing two-stage association and utilizing low confidence detections in the second stage. Recently proposed BoostTrack attempts to avoid the drawbacks of multiple stage association approach and use low-confidence detections by applying detection confidence boosting. In this paper, we identify the limitations of the confidence boost used in BoostTrack and propose a method to improve its performance. To construct a richer similarity measure and enable a better selection of true positive detections, we propose to use a combination of shape, Mahalanobis distance and novel soft BIoU similarity. We propose a soft detection confidence boost technique which calculates new confidence scores based on the similarity measure and the previous confidence scores, and we introduce varying similarity threshold to account for lower similarity measure between detections and tracklets which are not regularly updated. The proposed additions are mutually independent and can be used in any MOT algorithm. Combined with the BoostTrack+ baseline, our method achieves near state of the art results on the MOT17 dataset and new state of the art HOTA and IDF1 scores on the MOT20 dataset. The source code is available at: https://github.com/vukasin-stanojevic/BoostTrack .

本研究针对多目标跟踪中真实正检测框选择的不足，提出了一种改进的方法，以提升检测性能。论文提出了一种新的相似性度量方法，结合形状、马氏距离和软BIoU相似性，并引入了一种软检测置信度提升技术，使得置信度评分更为合理。该方法在MOT17和MOT20数据集上展示了显著的效果提升。 

BoostTrack++：利用轨迹信息在多目标跟踪中检测更多对象