摘要
为了解决在复杂背景以及人流密集且互相遮挡的场景下,对人流密度进行估计精度低的问题,提出了基于YOLOv3增强模型融合的方法进行人流密度估计.首先将数据集分别进行头部标注和身体标注,生成头部集和身体集.然后用这两个数据集分别训练两个YOLOv3增强模型YOLO-body和YOLO-head,最后使用这两个模型在相同的测试数据集上推理,将其输出结果进行极大值融合.结果表明基于YOLOv3增强模型融合的方法,与原始目标检测方法和密度图回归的方法相比精度提高了4%,且具有较好的鲁棒性.
The accuracy of crowd density estimation is low in complex backgrounds and the scenario with dense and mutually occluded crowds.To solve this,we propose a method based on YOLOv3 enhanced model fusion to estimate crowd density.The heads and bodies in the data set are labeled to generate head and body sets,which can then help train the two YOLOv3 enhanced models:YOLO-body and YOLO-head.Finally,the two models are reasoned on the same test data set,and their outputs are fused to the maximum value.Consequently,the method based on YOLOv3 enhanced model fusion has great robustness because its accuracy is 4%higher than that of original target detection and density map regression.
作者
孙乾宇
张振东
SUN Qian-Yu;ZHANG Zhen-Dong(School of Mechanical Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《计算机系统应用》
2021年第4期271-276,共6页
Computer Systems & Applications
