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.

这篇论文提出了一种名为 BiTrack 的 3D 离线多目标跟踪框架，它包括 2D-3D 检测融合、初始轨迹生成和双向轨迹重新优化模块，以实现从相机 - LiDAR 数据中获得最佳的跟踪结果。实验结果表明，BiTrack 在准确性和效率方面取得了 3D 离线多目标跟踪任务的最新成果。