In this work, we consider data association problems involving multi-object
tracking (MOT). In particular, we address the challenges arising from object
occlusions. We propose a framework called approximate dynamic programming track
(ADPTrack), which applies dynamic programming principles to improve an existing
method called the base heuristic. Given a set of tracks and the next target
frame, the base heuristic extends the tracks by matching them to the objects of
this target frame directly. In contrast, ADPTrack first processes a few
subsequent frames and applies the base heuristic starting from the next target
frame to obtain tentative tracks. It then leverages the tentative tracks to
match the objects of the target frame. This tends to reduce the occlusion-based
errors and leads to an improvement over the base heuristic. When tested on the
MOT17 video dataset, the proposed method demonstrates a 0.7% improvement in the
association accuracy (IDF1 metric) over a state-of-the-art method that is used
as the base heuristic. It also obtains improvements with respect to all the
other standard metrics. Empirically, we found that the improvements are
particularly pronounced in scenarios where the video data is obtained by
fixed-position cameras.

研究工作中，我们考虑了涉及多目标跟踪（MOT）的数据关联问题，特别是解决了物体遮挡带来的挑战。我们提出了一个名为近似动态规划跟踪（ADPTrack）的框架，它将动态规划原理应用于改进已有的基本启发式方法。在 MOT17 视频数据集上的测试中，该方法在关联准确性（IDF1 指标）上表现出了超过基本启发式方法的 0.7％的改进，并在其他所有标准指标上也有提升。经验上发现，改进在固定位置摄像头获取的视频数据场景中尤为显著。