Operator Behavior Analysis System for Operation Room Based on Deep Learning

Human behavior analysis has been a leading technology in computer vision in recent years. The station operation room is responsible for the dispatch of trains when they enter and leave the station. By analyzing the behaviors of the operators in the operation room, we can judge whether the operators have violations. However, there is no scheme to analyze the operator’s behavior in the operation room, so we propose an operator behavior analysis system in the station operation room to detect operator’s violations. This paper proposes an improved target tracking algorithm based on Deep-sort. The proposed algorithm can improve the target tracking performance through the actual test compared with the traditional Deep-sort algorithm. In addition, we put forward the detection scheme for common violations in the operation room: off-position, sleeping, and playing mobile phone. Finally, we verify that the proposed algorithm can detect the behaviors of operators in the station operation room in real time.