基于深度学习的客流量统计方法

针对目标快速移动和遮挡情况导致的客流统计存在误差的问题，设计目标的检测、跟踪、进门行为判断等策略，提出基于深度学习的餐饮业客流统计方法.首先，通过多数据集对YOLOv3-tiny模型进行训练，实现对于小目标的准确检测；进而设计多通道特征融合的目标跟踪算法，完成目标快速移动情况下的稳定跟踪；最后设计目标进门行为的判断方法，通过重叠率对目标的进门行为进行判断，实现对进门客流量的准确统计.最终通过实验验证，客流量统计的平均准确率达到93.5%.

Customer Flow Statistics Method Based on Deep Learning

Aiming at the problem of error in customer flow statistics caused by fast moving and shielding of the target when entering the door, this study designs the strategies of target detection, tracking, and behavior judgment of entering the door, and puts forward the method of customer flow statistics in catering industry based on deep learning. The YOLOv3-tiny model is trained by multi-data set, and the accurate detection of small target is realized. The target tracking algorithm of multi-channel feature fusion is designed to achieve the stable tracking in the case of fast target movement. In this study, we design a method to judge the entry behavior of the target through overlapping rate, and realize the accurate statistics of the entrance passenger flow. The experimental results show that the average accuracy rate of passenger flow statistics is 93.5%.