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

A technology of convolutional neural network and recognition method, which is applied in the field of driver behavior recognition based on multi-column fusion convolutional neural network, which can solve the problems of insufficient training samples and short research history of driver behavior recognition

Active Publication Date: 2018-11-23
SOUTHEAST UNIV
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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...

Method used

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

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Embodiment Construction

[0072] The technical solutions provided by the present invention will be described in detail below in conjunction with specific examples. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0073] The driver's behavior recognition method based on multi-column fusion convolutional neural network provided by the present invention, such as figure 2 shown, including the following steps:

[0074] Step 1: Collect data sets related to driver behavior recognition. The driver behavior recognition data comes from the network public data set KAGGLE-DRIVING (https: / / www.kaggle.com), which contains 22424 training pictures, including 10 categories such as figure 1 The different driving behaviors shown are:

[0075] C0: normal driving

[0076] C1: play with mobile phone - right hand

[0077] C2: Phone call - right hand

[0078] C3: Playing with mobile phone ...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V20/597G06N3/045
Inventor 路小波胡耀聪陆明琦
Owner SOUTHEAST UNIV
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