Identification of Abnormal Behavior in Electric Power Operations Using the Human Skeleton's Key Features

At present, the safety monitoring of electric power sites mainly monitors the monitoring video through personnel, which has problems such as heavy workload, missed detection, and false alarms. In order to realize the intelligent recognition of the behavior of personnel at the electric power operation site, an abnormal behavior recognition method based on the key points of human skeleton is proposed, which can realize the real-time detection of the behavior of personnel at the operation site. First, the backbone network of YOLOv5s is improved for target detection, and the trajectory of the target is obtained by combining the DeepSort target tracking algorithm; then, the AlphaPose attitude estimation method is used to extract the target skeleton sequence from the trajectory; finally, the multi-partition spatiotemporal graph convolutional network is used to track the personnel Abnormal behavior identification. The experimental results show that the recognition accuracy rate of the algorithm reaches 93.5%, and the processing speed reaches 18.32 fps, which can identify abnormal behaviors of electric power workers in real time and accurately.