Analysis and Study of Fatigue Driving Detection System based on Multimodal Fusion

Abstract

Fatigue driving always endangers road traffic safety and is one of the main causes of traffic accidents. How to effectively monitor and determine the fatigue driving state, so as to realize the fatigue early warning, has become a hot spot of scientific research. The traditional detection methods are mostly based on a certain human physiological parameters, through the monitoring of the physiological parameters changes, to complete the determination of the fatigue state. Compared with the traditional detection methods, the fatigue detection method based on computer vision has the advantages of accurate and reliable detection results, detection not relying on wearable devices, and good human-computer interaction experience. However, in the specific algorithm application scenarios, there are often interference from environmental factors such as partial occlusion of the inspected image, low image resolution, complex illumination of the detection environment, and unstable acquisition equipment. In the face of many challenges, how to effectively improve the detection accuracy of fatigue state, and the algorithm has the practical application space, still needs to be explored. The traditional fatigue detection method based on facial information often detects and analyzes a certain subordination. Due to the detection of individual differences and the fuzzy definition of fatigue state, the algorithm has the problems of high recognition error rate and poor robustness. Moreover, the single attribute model separates the potential integration and exclusion relationship between facial attributes. At the same time, the huge model size and complex model structure also limit the actual implementation of the algorithm. In view of these urgent problems, this paper first proposes the fusion model of face detection and head posture analysis based on the multi-task convolutional neural network (MTCNN). The model uses the architecture strategy of multi-task cascade to complete the complete facial posture analysis task while completing the facial detection task. Secondly, based on the lightweight convolutional neural network (SqueezeNet) of the network cascade, the facial key point detection network is designed to obtain the 72-point key point coordinates of the face. For the obtained facial attribute information, extract fatigue features and propose a fatigue determination method of multimodal fusion. Finally, combined with the research results, the construction of the online fatigue detection and early warning system platform was completed.