Research on substation inspection behavior recognition algorithm based on human feature points

With the rapid development of computer vision and machine learning, behavior recognition technology has become a research hotspot and is widely used in video surveillance, human-computer interaction, virtual reality and other fields. Traditional behavior recognition methods mostly rely on complex pre-processing and manual feature extraction, which has high computational cost and poor robustness. In order to overcome these limitations, this paper proposes a new behavior recognition algorithm based on human feature points. Firstly, the deep learning technology is used to realize the real-time positioning and tracking of the key feature points of the human body through the open pose framework. This technology can accurately identify the 34 key points of the human body under various postures and lighting conditions. Secondly, based on these key point data, we build a lightweight convolutional neural network model to analyze the dynamics of human behavior and capture the spatio-temporal characteristics of behavior. Through training and testing on the public human3.6m data set, our model achieves 92% accuracy in behavior recognition, and can process 30 frames of video stream per second, showing excellent real-time performance. This research not only improves the efficiency and accuracy of behavior recognition, but also provides a valuable reference for future deployment in practical applications.