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Drowsiness and fatigue of the drivers are amongst the significant causes of the car accidents. Every year the number of deaths and fatalities are tremendously increasing due to multifaceted issues and henceforth requires an intelligent processing system for accident avoidance. In relevant with this, an effective driver drowsiness detection system is proposed. The main challenges are robustness of the algorithm towards variation of the human face and real-time processing capability. The first challenge pertaining to the facial variation has been handled well using conventional image processing and hand-craft features of computer vision algorithms. Yet, variations such as facial expression, lighting condition, intra-class variation, and pose variation are additional issues that conventional method failed to address. Deep learning is an alternative solution which provides a better performance by learning features automatically. Thus, this paper proposed a new concept for handling the real-time driver drowsiness detection using the hybrid of convolutional neural network (CNN) and long short-term memory (LSTM). The performance of the system has been tested using the public drowsy driver dataset from ACCV 2016 competition. The results show that it can outperform the former schemes in the literature.

Recent advances in salient object detection adopting deep convolutional neural networks have achieved state-of-the-art performance. Salient object detection is task in computer vision to detect interesting objects. Most of the Convolutional Neural Network (CNN)-based methods produce plausible saliency outputs, yet with extra computational time. However in practical, the low computation algorithm is demanded. One approach to overcome this limitation is to resize the input into a smaller size to reduce the heavy computation in the backbone network. However, this process degrades the performance, and fails to capture the exact details of the saliency boundaries due to the downsampling process. A robust refinement strategy is needed to improve the final result where the refinement computation should be lower than that of the original prediction network. Consequently, a novel approach is proposed in this study using the original image gradient as a guide to detect and refine the saliency result. This approach lowers the computational cost by eliminating the huge computation in the backbone network, enabling flexibility for users in choosing a desired size with a more accurate boundary. The proposed method bridges the benefits of smaller computation and a clear result on the boundary. Extensive experiments have demonstrated that the proposed method is able to maintain the stability of the salient detection performance given a smaller input size with a desired output size and improvise the overall salient object detection result.