针对乘客在搭乘扶梯时的危险行为难以被实时准确检测的问题，提出了一种基于视频监控的手扶电梯乘客异常行为识别算法。首先，使用 YOLOv3 对图像中乘客的位置进行检测; 接着，使用 MobileNetv2 作为基网络，结合反卷积层对检测出来的乘客进行人体骨架提取; 然后，使用骨架距离作为跟踪依据，采用匈牙利匹配算法对相邻帧间的人体骨架进行匹配，实现视频中乘客的 ID 号分配; 最后，通过图卷积神经网络对乘客关键点信息进行异常行为识别。在 GTX1080GPU 上的实验结果表明，文中提出的识别算法的处理速度能达到 15f/s，异常行为识别准确率达 94. 3%，能够实时准确地识别手扶电梯上乘客的异常行为。

Aiming at the problem that dangerous behavior of passengers on the escalator is difficult to be accurately detected in real time，an algorithm for identifying abnormal behavior of escalator passengers based on video surveil-lance was proposed． Firstly，YOLOv3 was used to detect the position of the passenger in the image． Secondly，MobileNetv2 was used as the base network，which was combined with the deconvolution layer to extract the human skeleton of the detected passenger． Thirdly，the Hungarian assignment algorithm based on skeleton distance was used to realize the allocation of passenger ID numbers in the video． Finally，with keypoints as the input，the graph convolutional neural network was used to recognize the abnormal behavior of passenger． The experimental results on the GTX1080GPU show that the proposed recognition algorithm can achieve a processing speed of 15 f/s and an abnormal behavior recognition accuracy rate of 94. 3%，which can accurately recognize the abnormal behavior of passengers on the escalator in real time．