BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data
This work addresses offline 3D tracking for autonomous driving applications, presenting an incremental improvement with specific technical modules.
The paper tackled the problem of offline 3D multi-object tracking using camera-LiDAR data by proposing BiTrack, a framework that includes 2D-3D detection fusion, initial trajectory generation, and bidirectional re-optimization, achieving state-of-the-art performance on the KITTI dataset in terms of accuracy and efficiency.
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.