  3D Multi-object tracking (MOT) ensures consistency during continuous dynamic
detection, conducive to subsequent motion planning and navigation tasks in
autonomous driving. However, camera-based methods suffer in the case of
occlusions and it can be challenging to accurately track the irregular motion
of objects for LiDAR-based methods. Some fusion methods work well but do not
consider the untrustworthy issue of appearance features under occlusion. At the
same time, the false detection problem also significantly affects tracking. As
such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the
Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and
LiDAR data and significantly reduces tracking failures caused by occlusion and
false detection. For occlusion problems, we are the first to propose an
occlusion head to select the best object appearance features multiple times
effectively, reducing the influence of occlusions. To decrease the impact of
false detection in tracking, we design a motion cost matrix based on confidence
scores which improve the positioning and object prediction accuracy in 3D
space. As existing multi-object tracking methods only consider a single
category, we also propose to build a multi-category loss to implement
multi-object tracking in multi-category scenes. A series of validation
experiments are conducted on the KITTI and nuScenes tracking benchmarks. Our
proposed method achieves state-of-the-art performance and the lowest identity
switches (IDS) value (23 for Car and 137 for Pedestrian) among all multi-modal
MOT methods on the KITTI test dataset. And our proposed method achieves
state-of-the-art performance among all algorithms on the nuScenes test dataset
with 75.3% AMOTA.
