Blinking frequency and sight line estimation method based on eye region generation network

A blinking frequency and line of sight technology, applied in the field of driver fatigue driving warning, can solve the problems affecting subsequent feature extraction, time-consuming training, slow processing speed, etc., to meet real-time requirements, save processing time, and improve the effect of accuracy

Active Publication Date: 2018-11-02
XIAN UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

In the process of traditional target detection, the sliding window strategy is used to traverse the entire image, and different scales and different lengths and widths need to be set, which makes the complexity too high, produces too many redundant windows, and takes a long time
This seriously affects subsequent feature extraction
In the deep learning target detection algorithm of Region Proposal (candidate area), the extraction of candidate areas has been greatly improved, but it is still not close to the real...

Method used

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  • Blinking frequency and sight line estimation method based on eye region generation network
  • Blinking frequency and sight line estimation method based on eye region generation network
  • Blinking frequency and sight line estimation method based on eye region generation network

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Embodiment Construction

[0029] The present invention mainly relates to the combination of SSD target detection and ellipse fitting method, and the specific scheme is as follows:

[0030] Pre-training stage: SSD target detection to get the circumscribed rectangle of eyes and pupils

[0031] 1. If figure 1 As shown, SSD uses a feature pyramid structure for detection, that is, the feature maps (feature maps) of conv4-3, conv-7 (FC7), conv6-2, conv7-2, conv8_2, and conv9_2 are used for detection. Simultaneously perform softmax (regression classifier) ​​classification and position regression on multiple feature maps, which enables layer-by-layer prediction of feature images, and can quickly and accurately locate the effect of feature maps. The feature images mentioned here are thousands. The face pictures of the driver during the driving process can also be directly extracted from the dlib face feature library for training.

[0032] 2. A Prior Box (a network structure) is introduced into each convolutio...

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Abstract

The invention discloses a blinking frequency and sight line estimation method based on an eye region generation network. The method can be used for carrying out early warning on fatigue driving of a driver. Particularly, the method comprises the steps of: firstly, applying machine deep learning to extract an eye feature region of the driver, which is shot by a camera; carrying out driver eye simulation on the feature region by an ellipse fitting method; and determining a blinking frequency and pupil positions by combining perclos. The estimation method gives out the blinking frequency and a sight line direction of the driver and provides supporting bases for a driver fatigue monitoring system and driver dynamic behavior analysis.

Description

technical field [0001] The invention belongs to the field of automobile safety and relates to a method for estimating the driver's blinking frequency and line of sight, and the method can be used for early warning of driver's fatigue driving. Background technique [0002] At present, traffic accidents caused by fatigue driving still occupy the main component. Therefore, the successful early warning of drivers under driving fatigue has become a problem that people pay more and more attention to. This has also led most researchers to propose many effective auxiliary measures for this problem. The development of image processing technology and the extraction of facial features have provided a theoretical basis for the research on driver fatigue warning methods, but they are still immature. period. E.g: [0003] Since the target may appear anywhere in the image, and the size and aspect ratio of the target are also uncertain. In the process of traditional target detection, the...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/171G06V40/18G06V20/597G06N3/045
Inventor 赵栓峰许倩张传伟
Owner XIAN UNIV OF SCI & TECH
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