【摘要】 针对传统利用交并比（IOU）进行关联的多目标跟踪方法在目标遮挡下性能不佳以及特征重识别关联在处理外观相似目标时的局限性，提出了一种基于伪深度信息的层级匹配多目标跟踪算法。所提算法采用了立体几何方法获取图像中目标的伪深度信息，根据伪深度的大小，将检测框和轨迹划分为多个不同的子集。当一些目标相互遮挡但其伪深度差异较大时，它们将被分类到不同的伪深度等级中，从而避免了匹配冲突的问题。接下来，利用伪深度信息计算伪深度代价矩阵，在同一伪深度等级下执行交并比伪深度（IOU-D）匹配，以关联处于相同伪深度等级下的被遮挡目标。实验结果显示，所提算法在MOT17测试集和DanceTrack测试集上分别实现了65.1%和58.5%的高阶跟踪准确度（HOTA），与基线模型ByteTrack相比，在两个数据集上分别提升了2.0%和10.8%。该研究结果表明，有效利用图像中潜在的伪深度信息，可显著提升对被遮挡目标的跟踪准确性。更多还原

【Abstract】 A hierarchical matching multi-object tracking algorithm based on pseudo-depth information was proposed to address the performance limitations of traditional multi-object tracking methods that rely on intersection over union(IOU)for association under target occlusion, as well as the constraints of feature re-identification in dealing with visually similar objects. The proposed algorithm utilized a stereo geometric approach to acquire pseudo-depth information of objects in the image. Based on the magnitude of pseudo-depth, both the detection boxes and trajectories were divided into multiple distinct subsets. When some objects were occluded but had significant differences in pseudo-depth, they were classified into different pseudo-depth levels, thereby avoiding matching conflicts. Subsequently, a pseudo-depth cost matrix was computed using the pseudo-depth information, and an IOU pseudo-depth(IOU-D) matching was performed within the same pseudo-depth level to associate occluded targets located at the same pseudo-depth level. Experimental results show that the proposed algorithm achieved 65. 1% and 58. 5% higher order tracking accuracy(HOTA) on the MOT17 and DanceTrack test sets, respectively. Compared to the baseline model, ByteTrack, the proposed algorithm improved by 2. 0% and 10. 8% on the two data sets, respectively. Experimental results indicate that effectively utilizing the potential pseudo-depth information in the image can significantly enhance the tracking accuracy of occluded targets.更多还原