High-speed train driver alertness detecting method based on face image and eye movement analysis

A technology for high-speed trains and detection methods, applied in image analysis, image data processing, instruments, etc., can solve problems such as difficult to comprehensively analyze the driver's alertness state, accuracy and robustness need to be improved, and achieve high-efficiency facial features Effects of extraction, accuracy improvement and robustness

Inactive Publication Date: 2012-08-01
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

[0006] Existing alertness evaluation methods based on PERCLOS values ​​reveal that it is difficult to comprehensively analyze the driver...

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  • High-speed train driver alertness detecting method based on face image and eye movement analysis
  • High-speed train driver alertness detecting method based on face image and eye movement analysis
  • High-speed train driver alertness detecting method based on face image and eye movement analysis

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Embodiment

[0067] A specific embodiment of the present invention is a high-speed train driver alertness detection method based on facial image and eye movement analysis, comprising the following steps:

[0068] a. Face feature extraction:

[0069] Set the primary and secondary cameras with the same sampling frequency in front of the instrument panel of the high-speed train cab to collect the driver's head image, use the wavelet multi-scale analysis method to extract the head features of each frame of the head image of the main camera, and then use the neural network training method Quickly locate the driver's head; then use the AdaBoost algorithm to locate the driver's face for each frame of the head image;

[0070] For the head image where the face location fails, replace the head image with the head image of the corresponding frame captured by the secondary camera, and perform head feature extraction and face positioning on the head image of the corresponding frame captured by the seco...

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Abstract

Disclosed is a high-speed train driver alertness detecting method based on face image and eye movement analysis. The method includes steps of acquiring images of the head of a driver by a primary camera and a secondary camera simultaneously, extracting characteristics of the head of the driver step by step by the aid of wavelet multi-scale, recognizing the head of the driver via a neural network training method, and detecting the face of the driver via an AdaBoost arithmetic; respectively starting a Harr characteristic and a two-dimensional orthogonal Log-Gabor filtering phase characteristic to detect human eyes under different illuminations; building an adaptive non-linear level-log model of the driver; realizing eye movement tracking analysis for the driver via a strong tracking particle filter arithmetic; and finally realizing weight sum for an eye movement characteristic fatigue factor value and six face images including PERCLOS, pupilla, blinking, nodding, yawning and face inclination so as to obtain an alertness value of the driver. The calculated alertness value is high in accuracy and robustness.

Description

technical field [0001] The invention relates to an image detection, tracking and information fusion method, in particular to a method for tracking and analyzing facial images and eyes of high-speed train drivers. technical background [0002] The Beijing-Tianjin intercity high-speed railway with a design speed of 350 kilometers per hour was successfully put into operation on the eve of the opening of the 2008 Olympic Games. At 15:00 on June 30, 2011, it has a total length of 1318 kilometers and a design speed of 380 kilometers per hour. It is the longest high-speed passenger dedicated line in the world. The Shanghai high-speed railway was officially opened, and high-speed railways have developed rapidly in China. However, the safe operation and management of high-speed trains still faces enormous challenges. [0003] When a high-speed train is running, the locomotive signal and the ground signal on the line need to be perceived and understood by the driver, and make corresp...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06T7/20
Inventor 张祖涛李德芳张家树李恒建龙兵
Owner SOUTHWEST JIAOTONG UNIV
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