Abstract
In recent years, there have been an increasing number of traffic accidents which are caused by the abnormal driving behaviors, including dangerous driving and fatigue driving. This research aims to identify these abnormal driving behaviors immediately and warn the drivers by proposing a hybrid approach based on human pose estimation and facial key points detection. Initially, we utilize OpenPose neural network model to detect the key points of body, face and hand in real time. These data are employed to judge the dangerous driving behaviors such as smoking, phoning, etc. Furthermore, Dlib model are selected to detect facial key points, which are passed to fatigue detection algorithm PERCLOS. Facial features are extracted as a supplement to human pose data. Finally, our proposed hybrid method for abnormal driving detection can effectively reduce the risk of traffic accidents. Hence, it has a great social significance and application value in the transportation field.
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Acknowledgement
This work was supported by the Foundation of Innovation and Entrepreneurship Training Program for College Students, 2021 (No. 2021186Z) and High-level Innovation and Entrepreneurship Talents Introduction Program of Jiangsu Province of China, 2019.
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Ye, Z., Wu, Q., Zhao, X., Zhang, J., Yu, W., Fan, C. (2021). Abnormal Driving Detection Based on Human Pose Estimation and Facial Key Points Detection. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_8
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DOI: https://doi.org/10.1007/978-3-030-84529-2_8
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