A clsta-based behavior recognition method for railway drivers

Abstract

The invention discloses a CLSTA-based railway driver behavior recognition method, proposes a CLSTA neural network model, and transplants the CLSTA network into an industrial control computer, uses the monitoring video in the driver's room to recognize and understand the behavior of locomotive drivers, real-time Monitor and intelligently evaluate the driving behavior and driving state of the locomotive driver; use the convolutional neural network CNN and the long-short memory neural network LSTM to perform spatial feature learning and temporal feature learning on the video image of the locomotive driver's behavior, and consider the driver's indoor environment. The action changes little for the whole scene. In view of this situation, an improved spatio-temporal attention method STA is proposed, and a neural network model is obtained through a large amount of data set training. Finally, the model is applied to the industrial computer to analyze the driving process of the locomotive driver. Common behaviors and abnormal behaviors in the vehicle, such as fatigue driving, playing with mobile phones, smoking, etc., and finally achieve the purpose of understanding the behavior of locomotive drivers.

Description

technical field [0001] The invention relates to the technical field of railway traffic safety detection, in particular to a railway driver behavior recognition method based on CLSTA (ConvolutionalLSTM Networks With Spatial-Temporal Attention LSTM Convolutional Neural Network)). Background technique [0002] my country's railway construction is entering a period of high-speed development characterized by "leap-forward development", and locomotive operation safety technology has put forward higher requirements. How to ensure the smooth operation of locomotives has become the top priority of the railway transportation department, and it has become a top priority to improve and improve the monitoring and management level of the locomotive operation safety in the railway maintenance department. [0003] As we all know, in addition to sudden equipment failures such as axle cutting of locomotives, track breakage, etc., or natural disasters, the biggest threat to train operation saf...

AI Technical Summary

Problems solved by technology

[0004] my country's railway traffic safety monitoring has made great progress in recent years, but there is still a big gap compared with developed countries, mainly reflected in the accuracy and real-time monitoring of various information, and the lack of identification of the driver's personal working status. Alarm, the system function cannot meet the requirements, etc.

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