Saurav, S and Mathur, S and Sang, I and Prasad, SS and Singh, S
(2019)
Yawn Detection for Driver's Drowsiness Prediction using
Bi-Directional LSTM with CNN Features.
In: 11th International Conference on Intelligent Human Computer Interaction (IHCI-2019), December 12-14, 2019, IIT, Jodhpur, Rajasthan, India.
Abstract
Drowsiness of drivers is a critical problem and has recently attracted a lot of attention from both academia and industry. A real-time driver's drowsiness detection system is often considered as a crucial component of the Advanced Driver Assistance System (ADAS). Although, there are a number of physical parameters associated with drowsiness like blink frequency, eye closure duration, pose, gaze, etc., yawing can also be used as an indicator of drowsiness. This work presents a novel deep learning-based framework for driver's drowsiness prediction based on yawn detection in a video stream. The proposed approach uses a combination of a convolutional neural network (CNN), 1D-CNN, and bi-directional LSTM (Bi-LSTM) network. In the first step, the pipeline extracts the mouth region from each frame of the video using a combination of face and landmark detector. In the subsequent step, spatial information from the mouth region is extracted using a pre-trained deep convolutional neural network (DCNN). Finally, sequential information which models the evaluation of yawn using the extracted mouth feature is learned using a blend of 1D-CNN and bi-directional LSTM (Bi-LSTM) network. Experiments were performed on manually extracted and annotated video clips obtained from two publically available drowsiness detection dataset namely YAWDD and NTHU-DD. Experimental results show the effectiveness of the pro-posed approach both in terms of recognition accuracy and computational efficiency. Thus, the proposed pipeline is a good candidate for real-time implementation on an embedded device.
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