Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT). It has long been known that appearance information plays an essential role in the detection-to-track association, which lies at the core of the tracking-by-detection paradigm. While most existing works consider the appearance distances between the detections and the tracks, they ignore the statistical information implied by the historical appearance distance records in the tracks, which can be particularly useful when a detection has similar distances with two or more tracks. In this work, we propose a hybrid track association (HTA) algorithm that models the historical appearance distances of a track with an incremental Gaussian mixture model (IGMM) and incorporates the derived statistical information into the calculation of the detection-to-track association cost. Experimental results on three MOT benchmarks confirm that HTA effectively improves the target identification performance with a small compromise to the tracking speed. Additionally, compared to many state-of-the-art trackers, the DeepSORT tracker equipped with HTA achieves better or comparable performance in terms of the balance of tracking quality and speed.

在深度神经网络对象检测的最新进展的推动下，按检测跟踪的范例在多对象跟踪（MOT）的研究社区中越来越流行。早就知道，外观信息在“检测到跟踪”关联中起着至关重要的作用，而“检测到跟踪”关联处于“逐个检测跟踪”范式的核心。尽管大多数现有作品都考虑了检测与轨道之间的出现距离，但它们忽略了轨道中历史出现距离记录所隐含的统计信息，当检测到两个或多个轨道具有相似的距离时，这特别有用。在这项工作中，我们提出了一种混合轨道关联（HTA）算法，该算法使用增量高斯混合模型（IGMM）对轨道的历史出现距离进行建模，并将派生的统计信息纳入检测到轨道关联成本的计算中。在三个MOT基准测试上的实验结果证实，HTA在不影响跟踪速度的前提下有效地提高了目标识别性能。此外，与许多最新的跟踪器相比，配备了HTA的DeepSORT跟踪器在跟踪质量和速度之间取得了平衡，从而获得了更好或相当的性能。在三个MOT基准测试上的实验结果证实，HTA在不影响跟踪速度的前提下有效地提高了目标识别性能。此外，与许多最新的跟踪器相比，配备了HTA的DeepSORT跟踪器在跟踪质量和速度之间取得了平衡，从而获得了更好或相当的性能。在三个MOT基准测试上的实验结果证实，HTA在不影响跟踪速度的前提下有效地提高了目标识别性能。此外，与许多最新的跟踪器相比，配备了HTA的DeepSORT跟踪器在跟踪质量和速度之间取得了平衡，从而获得了更好或相当的性能。