Three-dimensional vehicle multi-target tracking based on trajectory optimization
In order to solve the problem of poor tracking effect of multi-target tracking algorithm in the case of occlusion,a multi-target tracking algorithm based on 3D point cloud detection is proposed.The 3D target detector based on point cloud is used to detect the vehicle target and obtain the location information of the 3D target;The target position in the next frame is predicted by tracking the target position in the current frame through a three-dimensional Kalman filter;The intersection ratio of 3D center point space distance and cross-union ratio of bird's eye view is fused as the weight,and the improved Hungarian algorithm is used for data association;Aiming at the problem of label switching before and after occlusion,a trajectory optimization algorithm is proposed.Experiments were conducted on KITTI dataset,and the vehicle tracking accuracy and tracking accuracy reached 84.71％and 86.63％respectively.Under the same threshold,this method is 6.28％and 0.39％higher than AB3DMOT respectively.Experimental results show that this algorithm can effectively improve the performance of 3D multi-target tracking.
万方数据
维普
