BiTrack：使用相机-LiDAR数据的双向离线3D多目标跟踪
BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data
AI总结：
本文提出BiTrack框架，通过2D-3D检测融合、双向轨迹生成与贪婪重优化，利用相机-LiDAR数据实现高精度3D离线多目标跟踪，并在KITTI上达到先进性能。
摘要：
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、错误连接修正和完整轨迹优化的优势，但必须应对边界框不对齐以及轨迹评估、编辑和细化所带来的挑战。本文提出“BiTrack”，这是一个3D OMOT框架，包含2D-3D检测融合、初始轨迹生成和双向轨迹重优化模块，以利用相机-LiDAR数据获得最优跟踪结果。本文的新颖性包括三个方面：（1）开发了一种点级目标配准技术，该技术采用基于密度的相似性度量来实现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技术，利用基于顶点的相似性度量以及虚警剔除和轨迹恢复机制，生成可靠的双向目标轨迹；（3）开发了一种轨迹重优化方案，以贪婪方式重新组织不同保真度的轨迹片段，并通过补全和平滑技术细化每条轨迹。在KITTI数据集上的实验结果表明，BiTrack在准确性和效率方面均达到了3D OMOT任务的最先进性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
