Abstract
One of the major causes of road accidents is driver distraction. Driver distraction is diversion of attention away from activities critical for safe driving. Driver distraction can be categorized into drowsiness and inattentiveness. Drowsiness is a condition in which the driver feels sleepy, therefore cannot pay attention toward road. Inattentiveness is diversion of driver’s attention away from the road. Our system provides facility for monitoring driver’s activities continuously. The in-car camera is mounted to capture live video of driver. Viola–Jones algorithm is used to identify the driver’s non-front-facing frames from video. Inattentiveness is detected if the system identifies consecutive frames having non-frontal face. Drowsiness is identified by continuous monitoring of the eye status, which is either “open” or “closed” using horizontal mean intensity plot of eye region. Once the system detects the distraction, alert is generated in the form of audio. This will reduce the risk of falling asleep in long distance traveling during day and night time.
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Acknowledgements
The authors express their deep sense of gratitude and indebtedness to Dr. Amol R. Madane (TCS) who was very kind to provide us with an opportunity to work under his immense expertise. His prompt inspiration, suggestions with kindness and dynamism enabled us to shape the present work as it shows. We would like to express our sincere gratitude toward our project guide Dr. A. M. Deshpande for her constant encouragement and valuable guidance.
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Modak, A., Paradkar, S., Manwatkar, S., Madane, A.R., Deshpande, A.M. (2020). Human Head Pose and Eye State Based Driver Distraction Monitoring System. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_34
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DOI: https://doi.org/10.1007/978-981-32-9088-4_34
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