Abstract:  In order to accurately identify passengers’ abnormal behaviors such as fighting in the closed and narrow elevator car， and avoid the occurrence of safety accidents， a passenger abnormal behavior identification method based on the joint spatio-temporal features of human skeleton is proposed. Firstly， this method uses YOLOv7 to detect the passenger’s position in the video， extracts the coordinates of the key points of the skeleton through the YOLOv7-Pose pose estimation algorithm， and filter out the complex background interference. Secondly， for the features of large amplitude， fast speed， and chaotic direction of the abnormal behaviors， we use the SURF joint pyramid hierarchical improvement of the LK optical flow method to carry out joint temporal and spatial feature extraction of the passenger’s human skeleton information. Finally， the optical flow changes of the feature points can be used to judge whether abnormal behavior occurs in the car and alarm it in time. The dataset in this paper is derived from the self-constructed dataset in elevator scenario and the behavioral public dataset in non-elevator scenario respectively. After experimental validation， the accuracy of this method on the recognition of abnormal behaviors reaches 95.53%， which is improved in speed and accuracy compared with other methods. It can meet the real-time requirements and be applied to the video monitoring system of elevator car to ensure the safety of the passengers in the elevator.

Key words: elevator car, abnormal behavior recognition, YOLOv7, pose estimation, feature extraction, pyramid layered LK optical flow method