Research on Abnormal Driver Behaviors Monitoring based on Bone Key Points

Yiwen Fang

doi:10.62051/c1gd5y86

Authors

Yiwen Fang

DOI:

https://doi.org/10.62051/c1gd5y86

Keywords:

Human position estimation, Bone key points, Behavior detection.

Abstract

With the development of social economy, the number of road vehicles is increasing, and road traffic safety has become one of the major factors threatening people's life and property safety. Abnormal driving behavior of drivers represented by mobile phone operation is one of the important causes of road traffic safety accidents. Real-time monitoring of drivers' mobile phone use and implementing appropriate measures when abnormal behavior occurs are of great significance to protect the life and property safety of drivers and passengers. Due to the differences in the driver's own appearance, the various ways of operating the mobile phone, and the chaotic background in the car, the routine abnormal behavior monitoring is prone to wrong detection and missing detection. Based on the existing human pose estimation model, this paper proposes to classify the driver's mobile phone use by using the method of calculating the Angle between the arm and the bone key points: Did not use the mobile phone, answer the phone, edit the information, to prepare for the subsequent use of the model classification. Based on this model, it can be extended to monitor other abnormal behaviors of drivers. It has practical application value to promote civilized driving and improve vehicle safety.

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References

The number of motor vehicles in China reached 435 million, with 523 million drivers, and the number of new energy vehicles exceeded 20 million on https://www.mps.gov.cn/n2254314/n6409334/c9384907/content.html

M. Paolo, S. Pasquale, N. Raffaele, D.R. Donatella, B. Alessandro, S. Antonia, D.R. Elvira, T. Maria, The use of mobile phone while driving: Behavior and determinant analysis in one of the largest metropolitan area of Italy, Accident Analysis and Prevention. 157 (2021) 106161-106161.

Traffic Management Bureau of the Ministry of Public Security, Annual Statistical Report of Road Traffic Accidents of the People's Republic of China (2019). (2020).

J. Zhang, S. Ma, Reserch on Analysis Method of Major Traffic Accidents Causes, Journal of People's Public Security University of China (Natural Science Edition), 03 (2023) 45-51.

C. Wang, H. Zhang, Research on Abnormal Behavior Detection of Drivers and Passengers in Car, Value Engineering, 41 (2022) 163-165.

J. Zhao, Driver distraction detection based on deep learning, Hangzhou Dianzi University (2022).

H. Li, Research and Design of a Driving Behavior Detection System Based on Deep Learning, Shanxi University of Science and Technology (2023).

X. Wang, Research on Intelligent Monitoring Method of Drivers' Bad Driving Behavior Based on Deep Learning, Dalian Maritime University (2023).

H. Wang, Z. Li, P. Gu, A Lightweight Network for Human Pose Estimation Based on YOLOPose, Journal of Shenyang Ligong University, 42 (2023) 10-16.

L. Zhu, W. Wang, Human behavior analysis based on skeleton model, Electronic Measurement Technology, 42 (2019) 68-73.

J. Li, D. Wang, S. Zhang, Deep-Learning-Based 2D Human Pose Estimation: Present and Future, Chinese Journal of Computers, 47 (2024) 231-250.

Y. Zou, N. He, Y. Guo, H. Liu, Review of Human Pose Estimation Based on Deep Learning, China Computer Users Association network application branch. Proceedings of the 27th Annual Conference on New Network Technologies and Applications, China Computer Users Association Network Application Branch, 2023. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, (2023).51-54.

X. Ren, J. Wang, H. Chen, Analysis of 2D Human Pose Estimation Based on Deep Learning, Changjiang Information & Communications, 37 (2024) 40-43.

X. Jia, W. Zhang, X. Yang, Robust estimation of human posture under visual occlusion, High-tech Communication, 31 (2021) 1210-1218.

Z. Zhu, Q. Mi, W. Ma, S. Xv, X. Zhang, Part-Level Occlusion-Aware Human Pose Estimation, Journal og Computer Research and Development, 59 (2022) 2760-2769.

G. Han, H. Wang, S. Wang, E. Chen, Joint Keypoint Data Augmentation and Structural Prior for Occluded Human Pose Estimation, Computer Engineering and Applications, (2024) 1-10.

S. Zheng, Research on Algorithm of Pedestrian Attitude Estimation and Recognition Based on Machine Learning, Shandong University (2019).

S. Liu, R. Ma, J. He, F. Fang, The Identify of Human Postures Based on an Improved YOLO V7, FUJIAN NONGJI, 04 (2023) 19-24.

Q. Yang, Research on Driver Abnormal Behavior Detection based on Multi-information Key Points, Chongqing Jiaotong University (2023).

X. Li, Research on Driver's Hand-held Call Status Detection Method based on Deep Learning, China University of Mining and Technology (2020).