In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calculation in the data association stage. Namely, the inherent uncertainties existing in tracks and detections are overlooked. In this work, we discard the commonly used deterministic tracks and deterministic detections for data association, instead, we propose to model tracks and detections as random vectors in which uncertainties are taken into account. Then, based on the Jensen-Shannon divergence, the similarity between two multidimensional distributions, i.e. track and detection, is evaluated for data association purposes. Lastly, the level of track uncertainty is incorporated in our cost function design to guide the data association process. Comparative experiments have been conducted on two typical datasets, KITTI and nuScenes, and the results indicated that our proposed method outperformed the compared state-of-the-art 3D tracking algorithms. For the benefit of the community, our code has been made available at https://github.com/hejiawei2023/UG3DMOT.

翻译：在现有文献中，大多数基于检测-跟踪框架的三维多目标跟踪算法在数据关联阶段采用确定性轨迹和检测进行相似度计算，即忽略了轨迹和检测中存在的固有不确定性。本文摒弃了常用的确定性轨迹与检测进行数据关联的方法，提出将轨迹和检测建模为考虑不确定性的随机向量。随后，基于Jensen-Shannon散度，评估两个多维分布（即轨迹与检测）之间的相似度以完成数据关联。最后，将轨迹不确定性水平纳入代价函数设计，从而引导数据关联过程。在两个典型数据集KITTI和nuScenes上进行了对比实验，结果表明，所提出的方法优于当前最先进的3D跟踪算法。为便于学界使用，相关代码已开源发布于https://github.com/hejiawei2023/UG3DMOT。