Intelligent vehicle-mounted fatigue monitoring method

Abstract

The invention provides an intelligent vehicle-mounted fatigue monitoring method and relates to the field of automobile safety. The system is characterized in that the system comprises a vehicle-mounted end, a cloud end, a user mobile end and a supervision end; the vehicle-mounted end comprises a video acquisition module, a coding processing module, an algorithm processing module, a sensor system,a central processor and an operation interface; the cloud end comprises a data transmission device, a cloud end data receiving device and a cloud end processor; the supervision end comprises a supervision department data transmission device and a supervision department processor; and the user mobile end comprises a mobile end data transmission module and a mobile end operation interface. The method is advantageous in that the monitoring is precise and reliable; the method has big data analysis and supervision functions; a traditional algorithm is improved; and traps in the algorithm are avoided.

Description

technical field [0001] The invention relates to the field of automobile safety, in particular to an intelligent vehicle fatigue monitoring system. Background technique [0002] How to provide an effective and practical safety driving assistance system for the driver is an important issue for safe driving of vehicles. The fatigue driving detection technology based on machine vision has been extensively studied at home and abroad, among which the detection of driver's eye features is the most extensive. [0003] When driving daily, we will encounter too little sleep time and poor sleep quality the day before; road conditions are so good that the road conditions are monotonous; encountering wind, sand, rain, fog, and snow weather conditions; driving for a long time and long distance; driving too fast or too slow; there is a time limit to reach the destination, etc., these will be the factors that induce your fatigue driving, and even the vehicle itself or the noise and vibrati...

AI Technical Summary

Problems solved by technology

[0006] 1. Inaccurate monitoring: The existing fatigue monitoring methods either use sensors for monitoring or use simple image processing

Both of these often have certain defects, which often lead to very inaccurate monitoring results, resulting in false alarms

[0007] 2. Lack of big data analysis: Since many drivers' fatigue driving is a long-term process, if there is no long-term analysis for drivers

There is also a lack of third-party supervision and management of drivers, resulting in many long-term fatigue drivers not being alerted, and thus unable to prevent problems before they happen

[0008] 3. Backward algorithms: most of the existing fatigue monitoring methods use old image algorithms

However, due to the great difference between fatigue monitoring and ordinary image recognition

If the old algorithm is used blindly, it will directly lead to inaccurate monitoring results and complex algorithms, and the processing is very slow

Images