Detection of Risky Riding Patterns of Motorcyclists based on Deep Learning and Linear Regression

Vattiya Jarunakarint, Surapong Uttama

Abstract


Motorcycle accidents are the most fatal road accidents found in many regions especially in Asian countries. From the accident statistics, a major cause is due to riders’ riding behaviors such as fast, drunk or reckless ridings which are generally defined as abnormal riding. Detection of such riding pattern is challenging and would be beneficial for preventing possible road accidents. This paper proposes a novel framework to detect abnormal riding in three risky cases: weaving, swerving and drifting from recorded video footages. The methodology comprises of two main steps. First we localized motorcycles in video frames using Convolution Neural Network with a model namely ‘rfcn_resnet101_coco’. Second all detected centroids of motorcycles were fitted with two linear regression models i.e. Ordinary least square (OLS) and Random sample consensus (RANSAC) to find their linearity. The riding patterns whose regression scores are high tends to be normal ridings. From experiments, OLS and RANSAC showed a good performance to differentiate between normal and abnormal driving. The thresholds around 0.95 for OLS score or R squared and 0.94 for RANSAC score are sufficient for this classification. In addition, RANSAC provided an advantage over OLS when there exist noises e.g. nearby parking motorcycles.


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References


Limited, B. (2019, Feb 13). Motorcycles key to solving road deaths. Retrieved November 30, 2020, from 217 https://www.bangkokpost.com/opinion/opinion/1628238/motorbikes-key-to-solving-road-deaths

Washington, D.C.: U.S. Dept. of Transportation, National Highway Traffic Safety Administration. (1998). The visual detection of DWI motorists.

Kim, K., Kim, B. W., Lee, J. W., & Park, D. (2018). Driver Reaction Acceptance and Evaluation to 221 Abnormal Driving Situations. 2018 International Conference on Information and Communication 222 Technology Convergence (ICTC), 1377-1379. doi:10.1109/ictc.2018.8539705

Yu, J., Chen, Z., Zhu, Y., Chen, Y., Kong, L., & Li, M. (2017). Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones. IEEE Transactions on Mobile Computing, 16(8), 2198-2212. doi:10.1109/tmc.2016.2618873

Hu, J., Zhang, X., & Maybank, S. (2020). Abnormal Driving Detection With Normalized Driving Behavior Data: A Deep Learning Approach. IEEE Transactions on Vehicular Technology, 69(7), 6943-6951. doi:10.1109/tvt.2020.2993247

Wu, X., Zhou, J., An, J., & Yang, Y. (2018). Abnormal driving behavior detection for bus based on the Bayesian classifier. 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI), 266-272. doi:10.1109/icaci.2018.8377618

Harkous, H., & Artail, H. (2019). A Two-Stage Machine Learning Method for Highly-Accurate Drunk Driving Detection. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). doi:10.1109/wimob.2019.8923366

Sandeep, K., Ravikumar, P., & Ranjith, S. (2017). Novel Drunken Driving Detection and Prevention Models Using Internet of Things. 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT). doi:10.1109/icrteect.2017.38

Al-Sultan, S., Al-Bayatti, A. H., & Zedan, H. (2013). Context-Aware Driver Behavior Detection System in Intelligent Transportation Systems. IEEE Transactions on Vehicular Technology, 62(9), 4264–4275. https://doi.org/10.1109/tvt.2013.2263400

Jarunakarint, V., Uttama, S., & Rueangsirarak, W. (in press). Survey and Experimental Comparison of Machine Learning Models for Motorcycle Detection. The 5th International Conference on Information Technology: InCIT2020.

Dai, J., Li∗, Y., He, K., & Sun, J. (2016, May). R-FCN: Object Detection via Region-based Fully Convolutional Networks. Advances in Neural Information Processing Systems.

Choi, S., Kim, T., & Yu, W. (2009). Performance Evaluation of RANSAC Family. Procedings of the British Machine Vision Conference 2009. https://doi.org/10.5244/c.23.81

Pedregosa, F., Varoquaux, Ga"el, Gramfort, A., Michel, V., Thirion, B., Grisel, O., … others. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825–2830.

Jang, S., & Ahn, B. (2020). Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT. Sustainability, 12(7), 3037. doi:10.3390/su12073037


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