FusionTrack: An Online 3D Multi-object Tracking Framework Based on Camera-LiDAR Fusion

3D multi-object tracking is an important component of the perception module in autonomous driving systems. Due to the limitations of a single sensor, tracking methods based on either LiDAR or cameras always have certain deficiencies. Fusion-based tracking methods have received increasing attention. However, existing fusion-based tracking methods often underutilize image information, ignore the respective effects of appearance information and 2D detection results, and lack further analysis on the simultaneous use of both. This paper proposes a novel camera-LiDAR fusion tracking framework that primarily relies on the motion model using 3D objects. It fully leverages the appearance information and 2D detection results simultaneously from images and introduces three modules to reduce the number of false positive samples, false negative samples and ID switches, respectively. Besides, the entire tracking process does not require global processing and achieves online tracking. The proposed method achieves competitive results on the KITTI tracking dataset with 78.50% HOTA. Compared with EagerMOT using the same 3D and 2D detectors, the HOTA metric improved by 4.11%. Code is available on https://github.com/zengwz/FusionTrack.