Locomotive driver behavior recognition method and device

Abstract

The present invention provides a locomotive driver behavior recognition method, device and system. The locomotive driver behavior recognition method includes: predefining a set of images to be trained containing multiple types of driver behaviors; using a neural network algorithm to model the set of images to be trained Training, generating a behavior training model; collecting real-time monitoring images of the locomotive cab, inputting the real-time monitoring images into the behavior training model, and identifying the category to which the driver's behavior belongs. The present invention can automatically identify several types of daily operations of the driver through the deep learning algorithm; at the same time, through the automatic image retrieval, the heavy manual work can be relieved, and the efficiency and accuracy of the analysis can be improved.

Description

technical field [0001] The invention relates to rail transit locomotive vehicle safety monitoring technology, in particular to a locomotive driver behavior recognition method, device and system in a driving cab. Background technique [0002] Traffic safety has now become a social issue that is generally concerned, and it is imminent to strengthen the control of railway traffic safety. At present, the video surveillance equipment of the locomotive on-board safety protection system (referred to as 6A system) is installed on the locomotive. At present, the video surveillance equipment The ground analysis task of video data usually adopts the method of manual browsing and viewing. On the one hand, analyzing the video recordings of all locomotives requires a large number of sufficient ground video data analysis personnel and equipment, and the analysis efficiency is low. Especially in the case of specific event search, the analysis The workload is huge and easily missed. On the ...

AI Technical Summary

Problems solved by technology

For example, when the locomotive is running, the vibration amplitude is relatively large, and the image is blurred, which affects the stability of the video image. The illumination changes during the train operation are relatively large, which greatly affects the accuracy of recognition. The recognition objects in the locomotive environment are quite different, which will also affect video recognition. adaptability, etc.

In addition, when the train enters and exits the station, there may be strong light conditions. At this time, the effect of video analysis and recognition will be reduced due to the influence of strong light. When the light returns to normal, the recognition effect will also improve. How to reduce the impact of illumination changes and improve the robustness of the algorithm is one of the technical difficulties faced by the industry

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