Method for detecting a mobile phone playing behavior of a driver based on deep learning

A detection method and deep learning technology, which is applied to computer parts, instruments, character and pattern recognition, etc., can solve the problems of detection accuracy, poor detection effect of non-rigid objects, and improvement of real-time performance, so as to improve detection Effects of real-time performance, improvement of real-time performance and accuracy, and reduction of calculation time

Active Publication Date: 2019-06-11
ZHEJIANG WANLI UNIV
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AI Technical Summary

Problems solved by technology

Among them, the traditional target detection method, that is, the detection method of artificially setting features combined with classifiers, has poor detection effect on non-rigid objects.
The target detection method of deep learning can detect non-rigid objects, but the detection accuracy needs to be improved, and the real-time performance also needs to be improved.

Method used

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  • Method for detecting a mobile phone playing behavior of a driver based on deep learning
  • Method for detecting a mobile phone playing behavior of a driver based on deep learning
  • Method for detecting a mobile phone playing behavior of a driver based on deep learning

Examples

Experimental program
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Effect test

Embodiment

[0046] Embodiment: a kind of detection method of the driver's mobile phone behavior based on deep learning, detection method comprises:

[0047] Collect the video sample of the driver playing with the mobile phone in the cab and convert it into a frame image, subtract the corresponding pixel values ​​to obtain a difference image, and then set the threshold to binarize the difference image. The threshold for the difference image binarization is 45-60 In the case of small changes in ambient brightness, if the corresponding pixel value changes less than the predetermined threshold, it can be considered as a background pixel; if the pixel value of the image area changes greatly, it can be considered that it is caused by a moving object in the image , and mark these areas as foreground pixels; the binarized dynamic target image undergoes subsequent operations such as filtering, expansion, erosion, etc., to remove isolated noise points, and obtain the dynamic target area; then the ob...

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Abstract

The invention discloses a method for detecting a mobile phone playing behavior of a driver based on deep learning. On the basis of deep learning for target detection, a detection result is obtained; Preprocessing is carried out on an acquired video and a convolutional neural network of the video is optimized. A video sample of a mobile phone played by a driver in a cab is collected and converted into a frame image; The method has the advantages that the real-time performance and accuracy of deep learning detection targets are greatly improved, the calculation time is shortened to a great extent, and the feature extraction accuracy of hands and mobile phones is greatly improved.

Description

technical field [0001] The invention relates to a detection method of a driver's behavior of playing with a mobile phone, in particular to a detection method of a driver's behavior of playing with a mobile phone based on deep learning. Background technique [0002] As the greatest invention of mankind in the 20th century, mobile phones have made people's communication easier and more convenient, but at the same time, they have gradually added many negative effects to our lives. For example, in terms of traffic safety, drivers often play with mobile phones on roads with few vehicles and while waiting at traffic lights. According to calculations, the driver’s response time to an emergency situation while playing with a mobile phone is 30% slower than that of drunk driving, and the probability of a traffic accident while playing with a mobile phone is about 4 times higher than that of normal driving. great safety hazard. [0003] Item 3 of Article 62 of the "Regulations for t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCY02D30/70
Inventor 朱仲杰金充充白永强张巧文
Owner ZHEJIANG WANLI UNIV
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