Driver behavior recognition method based on multi-column fusion convolutional neural network

Abstract

The invention provides a driver behavior recognition method based on a multi-column fusion convolutional neural network. The method comprises the following steps of: constructing a driver behavior recognition data set; performing data enhancement on a picture of the driver behavior recognition data set; constructing a deep learning architecture; training a designed deep learning model; and obtaining a recognition result by testing the model. The driver behavior recognition method based on the multi-column fusion convolutional neural network adopts the deep learning architecture, which is beneficial to extract more abstract hierarchical features for a driver behavior classification; and adopts the deep learning architecture of the convolutional neural network branch fusion with multiple different filter kernels, which is beneficial to extract image multi-scale features for the driver behavior classification. The multi-column fusion convolutional neural network designed in the driver behavior recognition method has more abstract local feature representation capability, can further improve the accuracy of the driver behavior recognition, and has important application value in public safety and intelligent transportation.

Description

technical field [0001] The invention belongs to the field of image processing and pattern recognition, and relates to a behavior recognition method, more specifically, to a driver behavior recognition method based on a multi-column fusion convolutional neural network. Background technique [0002] With the rapid development of the economy and the improvement of the material level of human beings, automobiles have become the most commonly used means of transportation. As the number of cars increases, traffic accidents occur more frequently. According to the official report of China's Ministry of Communications, in 2016, a total of 212,846 traffic accidents occurred and 63,093 people were killed. Irregular driving behavior has serious potential safety hazards, which is also the main reason for more than 80% of traffic accidents. Therefore, monitoring the driver's driving behavior has extremely important application value, which is also one of the key technologies of the Adva...

AI Technical Summary

Problems solved by technology

[0006] (3) The research history of driver behavior recognition is relatively short

The training samples in the current public data set are not sufficient, which also limits the further improvement of the recognition accuracy of the deep convolutional neural network.

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