Driver action recognition method and device based on three-dimensional convolutional neural network

A neural network and three-dimensional convolution technology, applied in the field of rail transit, can solve problems such as inaccurate judgment results, easy to be affected by occlusion or environment, etc.

Active Publication Date: 2019-12-06
TRAFFIC CONTROL TECH CO LTD
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Problems solved by technology

[0005] The embodiment of the present invention provides a driver action recognition method and device based on a three-dimensional convolutional neural network, which is used to solve the problem that th

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  • Driver action recognition method and device based on three-dimensional convolutional neural network
  • Driver action recognition method and device based on three-dimensional convolutional neural network
  • Driver action recognition method and device based on three-dimensional convolutional neural network

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Embodiment Construction

[0032] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0033] figure 1 It is a schematic flow chart of a driver action recognition method based on a three-dimensional convolutional neural network provided in this embodiment, see figure 1 , the method includes the following steps:

[0034] 101: Obtain the video captured by the driver of the train during the train running;

[0035] 102: Extract ...

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Abstract

The embodiment of the invention provides a driver action recognition method and device based on a three-dimensional convolutional neural network. Feature data is extracted from a video photographed bya driver through preset feature engineering, and a target model recognizes the behavior of the driver according to the feature data , wherein the target model is obtained by training a constructed three-dimensional convolutional neural network, the three-dimensional convolutional neural network comprises a plurality of combined layer structures connected in sequence, and each combined layer structure comprises a convolutional layer and a pooling layer. By improving the structure of the three-dimensional convolutional neural network, the trained target model has a more accurate recognition result on the action of the driver. And on the other hand, compared with facial feature acquisition, driver action acquisition is not easily disturbed by the environment, the feature data contains optical flow features reflecting time-dependent changes of driver actions, and the accuracy of an identification result is further improved through the action continuity data.

Description

technical field [0001] The present invention relates to the technical field of rail transit, in particular to a driver action recognition method and device based on a three-dimensional convolutional neural network. Background technique [0002] In ensuring the driving safety of urban rail transit, drivers shoulder important responsibilities, and their accurate actions and clear awareness often determine the safety of passenger transportation. Fewer driver configurations, monotonous driving actions, and high automation of train driving are important reasons for driver fatigue. At the same time, the driver's personal living habits, workload, and working hours will also affect whether the driver is fatigued. Some traditional methods are to alleviate the fatigue of train drivers by improving the management system and work plan. The train "anti-sleep" equipment also reduces the driver's fatigue to a certain extent. Sexual movements are not sensitive to the "anti-sleeping death" ...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V40/20G06V40/172G06V20/46G06V20/52G06N3/045
Inventor 罗铭肖骁
Owner TRAFFIC CONTROL TECH CO LTD
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