Accurately distinguishing each object is a fundamental goal of Multi-object
tracking (MOT) algorithms. However, achieving this goal still remains
challenging, primarily due to: (i) For crowded scenes with occluded objects,
the high overlap of object bounding boxes leads to confusion among closely
located objects. Nevertheless, humans naturally perceive the depth of elements
in a scene when observing 2D videos. Inspired by this, even though the bounding
boxes of objects are close on the camera plane, we can differentiate them in
the depth dimension, thereby establishing a 3D perception of the objects. (ii)
For videos with rapidly irregular camera motion, abrupt changes in object
positions can result in ID switches. However, if the camera pose are known, we
can compensate for the errors in linear motion models. In this paper, we
propose \textit{DepthMOT}, which achieves: (i) detecting and estimating scene
depth map \textit{end-to-end}, (ii) compensating the irregular camera motion by
camera pose estimation. Extensive experiments demonstrate the superior
performance of DepthMOT in VisDrone-MOT and UAVDT datasets. The code will be
available at https://github.com/JackWoo0831/DepthMOT.

通过实现深度感知和相机姿态估计，本文提出了 DepthMOT 算法，可以准确区分多个对象，并解决了拥挤场景中物体混淆和相机运动不规则导致的 ID 切换问题。在 VisDrone-MOT 和 UAVDT 数据集上的广泛实验表明 DepthMOT 具有卓越性能。