Driver drowsiness detection in video sequences using hybrid selection of deep features

Monitoring driver's drowsiness is a complex problem that involves many indicators whether behavioral or physiological. Drowsiness is a challenging problem that can lead to road disasters. Sleeping driver is more dangerous on the road than a speeding driver. Many statistics showed that one-fifth of road accidents in the world were due to driver fatigue, hence safety modules that can alert drowsy drivers in the hopes of reducing the risk of accidents are very important. This paper proposes a framework for driver drowsiness detection based on a computer vision solution. The proposed framework's first task is to detect the driver's face. A transfer learning is then performed for extracting the deep features from the driver's face image using a pre-trained deep convolutional network model trained on a facial recognition dataset. The previous tasks are applied in a sliding temporal window (less than a second) in which the frames are sampled. In this work, 9 frames were the best choice. The extracted features of these frames represent the observation matrix. Then temporal feature aggregation is applied to construct the raw feature vector. To obtain the final feature vector, a proposed feature selection is applied to omit possible irrelevant features. The final feature vector is finally fed to a binary classifier to decide whether there is drowsiness or not. Extensive experiments are applied to NTHU Drowsy Driver Detection (NTHU-DDD) video dataset. The outcomes show the outperformance of the proposed approach compared with the state-of-the-art approaches. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Monitoring driver's drowsiness is crucial for road safety. This paper presents a computer vision-based framework for driver drowsiness detection, which detects the driver's face, extracts deep features, applies temporal feature aggregation and feature selection, and uses a binary classifier to determine drowsiness.