CLAMOT: 3D Detection and Tracking via Multi-modal Feature Aggregation
SHUO ZHANG, XIAOLONG LIU, Wenqi Tao · 2022
In autonomous driving, multi-object tracking (MOT) can help vehicles perceive surroundings better and perform well-informed motion-planning. Methods based on LiDAR suffer from the sparsity of LiDAR points and detect only in a limited range. To this end, we propose a camera and LiDAR aggregation module named CLA-fusion to fuse the two modal features in a point-wise manner. The enhanced points can be used for extracting features through a 3D backbone. For the detection, we adopts a center-based method which means detecting the centers of objects by a keypoint detector and regressing other attributes, like 3D size, velocity, etc. In the tracking part, we use a simple but effective matching strategy, closest-point matching. According to the structure and characteristics of the whole framework, we name our model CLAMOT. Our experiments on nuScenes and Waymo benchmarks achieve competitive results.