An elevator passenger behavior recognition method based on two-stream convolution neural network

Abstract

In order to reduce elevator accidents caused by passengers’ incorrect use, a method for elevator passenger dangerous behaviour recognition based on two-stream convolutional neural network is proposed after analysing existing elevator passenger behavior recognition methods. A two-stream convolution neural network model framework and its parameters for elevator passenger behavior recognition are also given. Herein, the appearance features of human behavior are extracted from space domain, and the motion features of human behavior are extracted from time domain. Finally, the softmax outputs of the two-stream are merged with the liner weighting to realize human behavior recognition. The model is trained and evaluated on the video dataset of elevator passenger behavior. The results show that this method can be used to identify the unsafe behavior of elevator passengers, and the average recognition rate is 96.87%.