Multi-feature fusion driver abnormal expression recognition method

Abstract

The invention discloses a multi-feature fusion driver abnormal expression recognition method. The multi-feature fusion driver abnormal expression recognition method comprises the steps: S1, tracking and monitoring expression actions of a driver in real time through a camera installed on the driver side; S2, precisely identifying expression details in the real-time driver video; S3, detecting the positions of the eyes, and judging whether the eyes are tired or not; S4, positioning the edge contour of the mouth, and judging whether yawn action occurs or not; S5, detecting a head action, and judging whether fatigue occurs or not; S6, weighting the detection results of the eye state, the mouth state and the head motion state, finally judging whether fatigue occurs or not, and outputting the detection results; and S7, combining the identification result of the current frame as an estimated position of subsequent frame identification, and respectively detecting actions in subsequent frames to realize continuous detection and identification of abnormal behaviors of the driver. According to the invention, real-time monitoring and alarm triggering can be carried out, a driver is warned andreminded, and traffic accidents are prevented, and the safety in the driving process is ensured.

Description

technical field [0001] The invention relates to the fields of fatigue detection, human-computer interaction and video image pattern recognition, in particular to a multi-feature fusion driver abnormal expression recognition method. Background technique [0002] The road traffic in modern society is becoming more and more prosperous, and the traffic accidents that follow are showing a trend of frequent occurrence. Fatigue driving is an important cause of traffic accidents. How to effectively track the driver's fatigue driving and call the police is the key to reducing the occurrence of traffic accidents. an important measure. Among them, the abnormal driving behavior detection based on computer vision monitoring is a new research hotspot and direction. Novidia has launched the AI ​​Co-Pilot assisted driving system, which uses the on-board camera to obtain the driver's image video, combined with artificial intelligence algorithms Face recognition, head tracking, and eye movem...

AI Technical Summary

Problems solved by technology

When the vehicle passes through a road section with poor signal, data transmission will be delayed

Invention 201610835397.5 uses the method of blink detection and recognition for fatigue monitoring, but lacks the recognition of other fatigue behaviors. 201610104142.1 discloses a method of using brain waves to identify driver fatigue behavior, but it needs to be worn, which is not easy to apply in practical applications

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