3D Multi-object Tracking Based on Simultaneous Optimization of Object Detection and Scene Flow Estimation

Most 3D multi-object tracking methods independently optimize target detection and inter-frame data association, without considering the coupling between single-frame feature learning and inter-frame association learning. To achieve the coupled learning of single-frame detection and inter-frame association, a 3D multi-object tracking framework is proposed based on the joint optimization of target detection and scene flow estimation, named FlowDet-Track. In this framework, a detection-guided scene flow estimation module is introduced to alleviate incorrect inter-frame association. To obtain more accurate scene flow labels, especially in cases of rotational motion, a box transformation-based ground truth calculation method is proposed for scene flow. The experimental results on the KITTI MOT dataset indicate that the HOTA and DetA metrics for vehicle category is improved by 25.03% and 30.8% compared with PointTrackNet algorithm, demonstrating the superior performance of the proposed method in position tracking. Moreover, comparative experiments under extreme rotational motion further validate the algorithm robustness.