基于改进YOLOV7的变规模网络重叠区域多目标跟踪方法

Method of improved YOLOV7 based multitarget tracking for overlapping regions in variable scale networks

在实际场景中,目标之间常常存在重叠或部分遮挡的情况,若是未进行有效的多目标检测以及了解变规模网络重叠区域内的节点状况,会导致目标跟踪精度下降。对此,提出一种基于改进YOLOV7的变规模网络重叠区域多目标跟踪方法。首先,采用改进YOLOV7对变规模网络重叠区域多目标进行检测;然后,在目标检测的基础上,对多目标轨迹特征进行提取;最后,基于提取到的多目标轨迹特征,已知目标的速度、方向与距离,实现变规模网络重叠区域的多目标跟踪。实验结果表明,所提方法的跟踪精准度最高达到98%,曼哈顿距离明显小于对比方法,仅在0.1~-0.1之间,性能较优,具有实用性。

In practical scenarios,there is often overlap or partial occlusion between targets.If effective multitarget detection is not carried out to understand the node status in the overlapping area of the variable scale network,it will lead to a decrease in target tracking accuracy.Therefore,an improved YOLOV7 based multi target tracking method for the overlapping area of the variable scale network is proposed.The improved YOLOV7 is used to detect multiple targets in overlapping areas of the variable scale network.On the basis of target detection,multi target trajectory features are extracted.Multitarget tracking for overlapping areas in the variable scale network is realized based on the extracted multitarget trajectory features,and the given speed,direction,and distance of the targets.The experimental results show that the proposed method has a tracking accuracy of up to 98%,and the Manhattan distance is significantly smaller than that of the comparison method,only between 0.1 and-0.1,which has better performance and practicality.