Research on 3D multi-target tracking algorithm

Aiming at the problem of data association errors caused by frequent occlusion of vehicles in complex scenes and poor tracking results, a multi-target tracking algorithm based on Frustum PointNets is proposed. Firstly, the algorithm uses RGBD information and Frustum PointNets as the detector to detect the target's 3D bounding box. Secondly, the 3D Kalman state estimation is performed. Finally, the weighted Hungarian matching algorithm is used for data association to achieve accurate tracking in complex situations. Qualitative experiments and quantitative analysis on KITTI dataset show that this algorithm can effectively deal with occlusion, improve the accuracy of data association, and obtain 83.48% tracking precision and 80.71% tracking accuracy, which greatly improves the accuracy of target tracking algorithm.