基于轨迹优化的三维车辆多目标跟踪

Three-dimensional vehicle multi-target tracking based on trajectory optimization

针对多目标跟踪算法在目标遮挡情况下存在的跟踪效果不佳的问题,本文提出一种基于三维点云检测的多目标跟踪算法。采用基于点云的三维目标检测器检测车辆目标,获取三维目标的位置信息;通过三维卡尔曼滤波器结合当前帧跟踪目标位置预测其在下一帧的位置;融合三维中心点空间距离与鸟瞰视图的交并比作为权重,使用改进的匈牙利算法进行数据关联;针对遮挡前后目标发生标签切换问题,提出了轨迹优化算法。在KITTI数据集上进行实验,车辆类跟踪精度、跟踪准确度分别达到84.71%、86.63%。在同样阈值的情况下,该方法相比AB3DMOT分别提升了6.28%、0.39%。实验结果表明此算法能有效改善三维多目标跟踪性能。

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.