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%.