行人多目标跟踪算法

针对多目标跟踪中因目标遮挡而导致跟踪过程中身份交换频繁的问题, 提出一种行人多目标跟踪算法. 该算法首先使用YOLOv4作为检测器, 检测出目标并确定检测框坐标, 利用扩展Kalman滤波器对轨迹进行预测; 然后用匈牙利算法作为数据关联模块, 采用级联匹配方法将扩展Kalman滤波预测的检测框与目标检测的检测框进行匹配, 并将发生遮挡的目标加入轨迹异常修正算法； 最后在数据集MOT16的测试集上进行实验. 实验结果表明, 该算法取得了56.5%的跟踪准确度, 且对遮挡现象效果良好, 有效改进了对目标遮挡身份频繁切换以及遮挡引起的目标丢失的问题.

Pedestrian Multi-target Tracking Algorithm

Aiming at the problem of frequent identity exchange in multi-target tracking due to target occlusion, we proposed a pedestrian multi-target tracking algorithm. Firtsly, the algorithm used YOLOv4 as the detector to detect the target and determine the coordinates of the detection frame, the extended Kalman filter was used to predict the trajectory. Secondly, the Hungarian algorithm was used as the data association module, the detection frame predicted by extended Kalman filter was matched with the detection frame of target detection by cascade matching method, and the trajectory anomaly correction algorithm was added for the occluded target. Finally, the experiments were carried out on the test set of the MOT16 data set. The experimental results show that the algorithm achieves 56.5% tracking accuracy, and has a good effect on the occlusion phenomenon, which effectively improves the problem of frequent identity switching and target loss caused by occlusion.