Compared with real-time multi-object tracking (MOT), offline multi-object tracking (OMOT) has the advantages to perform 2D-3D detection fusion, erroneous link correction, and full track optimization but has to deal with the challenges from bounding box misalignment and track evaluation, editing, and refinement. This paper proposes "BiTrack", a 3D OMOT framework that includes modules of 2D-3D detection fusion, initial trajectory generation, and bidirectional trajectory re-optimization to achieve optimal tracking results from camera-LiDAR data. The novelty of this paper includes threefold: (1) development of a point-level object registration technique that employs a density-based similarity metric to achieve accurate fusion of 2D-3D detection results; (2) development of a set of data association and track management skills that utilizes a vertex-based similarity metric as well as false alarm rejection and track recovery mechanisms to generate reliable bidirectional object trajectories; (3) development of a trajectory re-optimization scheme that re-organizes track fragments of different fidelities in a greedy fashion, as well as refines each trajectory with completion and smoothing techniques. The experiment results on the KITTI dataset demonstrate that BiTrack achieves the state-of-the-art performance for 3D OMOT tasks in terms of accuracy and efficiency.

翻译：与实时多目标跟踪（MOT）相比，离线多目标跟踪（OMOT）具有执行2D-3D检测融合、错误关联校正及完整轨迹优化的优势，但必须处理边界框错位以及轨迹评估、编辑与优化带来的挑战。本文提出"BiTrack"——一个包含2D-3D检测融合、初始轨迹生成和双向轨迹重优化模块的三维OMOT框架，旨在从相机-激光雷达数据中获取最优跟踪结果。本文的创新性主要体现在三个方面：（1）开发了一种采用基于密度的相似性度量的点级目标配准技术，以实现2D-3D检测结果的精确融合；（2）开发了一套数据关联与轨迹管理方法，利用基于顶点的相似性度量及虚警抑制与轨迹恢复机制，生成可靠的双向目标轨迹；（3）设计了一种轨迹重优化方案，以贪婪方式重组不同置信度的轨迹片段，并采用补全与平滑技术对各轨迹进行精细化处理。在KITTI数据集上的实验结果表明，BiTrack在三维OMOT任务的准确性与效率方面均达到了最先进的性能水平。