This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions. Indeed, we propose a new real-time Depth Perspective-aware Multiple Object Tracking (DP-MOT) approach to tackle the occlusion problem in MOT. A simple yet efficient Subject-Ordered Depth Estimation (SODE) is first proposed to automatically order the depth positions of detected subjects in a 2D scene in an unsupervised manner. Using the output from SODE, a new Active pseudo-3D Kalman filter, a simple but effective extension of Kalman filter with dynamic control variables, is then proposed to dynamically update the movement of objects. In addition, a new high-order association approach is presented in the data association step to incorporate first-order and second-order relationships between the detected objects. The proposed approach consistently achieves state-of-the-art performance compared to recent MOT methods on standard MOT benchmarks.

翻译：本文旨在解决计算机视觉中的重要问题——多目标跟踪（MOT），该问题因诸多实际因素（尤其是遮挡）而仍具挑战性。为此，我们提出了一种新颖的实时深度感知感知多目标跟踪（DP-MOT）方法，以应对MOT中的遮挡难题。首先，我们提出了一种简单高效的基于主体排序的深度估计（SODE）方法，能够以无监督方式自动对二维场景中检测到的主体进行深度位置排序。基于SODE的输出，我们进一步提出了一种新颖的主动伪三维卡尔曼滤波器——一种带有动态控制变量的卡尔曼滤波器的简单但有效的扩展——用于动态更新目标的运动状态。此外，在数据关联步骤中，我们提出了一种新的高阶关联方法，以融合检测目标间的一阶和二阶关系。与近期MOT方法相比，所提方法在标准MOT基准上持续取得了最先进的性能。