Boosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check
This work addresses the problem of enhancing sensor fusion for more accurate tracking in autonomous driving, representing an incremental improvement over existing methods.
The paper tackles the challenge of improving 3D multi-object tracking for autonomous driving by integrating camera and radar data, achieving a 5-6% gain in IDF1 tracking performance on the K-Radaar dataset.
In the domain of autonomous driving, the integration of multi-modal perception techniques based on data from diverse sensors has demonstrated substantial progress. Effectively surpassing the capabilities of state-of-the-art single-modality detectors through sensor fusion remains an active challenge. This work leverages the respective advantages of cameras in perspective view and radars in Bird's Eye View (BEV) to greatly enhance overall detection and tracking performance. Our approach, Camera-Radar Associated Fusion Tracking Booster (CRAFTBooster), represents a pioneering effort to enhance radar-camera fusion in the tracking stage, contributing to improved 3D MOT accuracy. The superior experimental results on the K-Radaar dataset, which exhibit 5-6% on IDF1 tracking performance gain, validate the potential of effective sensor fusion in advancing autonomous driving.