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Drowsiness driving is a major cause of traffic accidents and thus numerousprevious researches have focused on driver drowsiness detection. Many driverelevant factors have been taken into consideration for fatigue detection andcan lead to high precision, but there are still several serious constraints,such as most existing models are environmentally susceptible. In this paper,fatigue detection is considered as temporal action detection problem instead ofimage classification. The proposed detection system can be divided into fourparts: (1) Localize the key patches of the detected driver picture which arecritical for fatigue detection and calculate the corresponding optical flow.(2) Contrast Limited Adaptive Histogram Equalization (CLAHE) is used in oursystem to reduce the impact of different light conditions. (3) Three individualtwo-stream networks combined with attention mechanism are designed for eachfeature to extract temporal information. (4) The outputs of the threesub-networks will be concatenated and sent to the fully-connected network,which judges the status of the driver. The drowsiness detection system istrained and evaluated on the famous Nation Tsing Hua University DriverDrowsiness Detection (NTHU-DDD) dataset and we obtain an accuracy of 94.46%,which outperforms most existing fatigue detection models.

Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection