Research on dangerous driving behavior recognition method based on convolutional neural network

Xiaochun Tong; Mary Jane C. Samonte

doi:10.1117/12.3050403

24 October 2024 Research on dangerous driving behavior recognition method based on convolutional neural network

Xiaochun Tong, Mary Jane C. Samonte

Author Affiliations +

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960S (2024) https://doi.org/10.1117/12.3050403
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

This paper discusses the research of dangerous driving behavior recognition method based on convolutional neural network (CNN). With the increase of car ownership, dangerous driving behavior has become an important hidden danger of road traffic safety. In order to improve road safety, this paper summarizes the research background, significance and specific methods of using CNN technology to identify dangerous driving behavior. In the research process, firstly, the driving behavior data was preprocessed to enhance the generalization ability of the model. In the model training stage, annotated data was used to optimize the model parameters. Then, the recognition process of driving behavior is analyzed, and a lightweight image classification model is analyzed and constructed. At the same time, three typical behavior recognition models are quantitatively compared to analyze their advantages and application scenarios, which provides a powerful reference for accurately identifying users' dangerous driving behaviors and provides a strong support for establishing a safe driving early warning model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaochun Tong and Mary Jane C. Samonte "Research on dangerous driving behavior recognition method based on convolutional neural network", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960S (24 October 2024); https://doi.org/10.1117/12.3050403

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KEYWORDS

Video

Data modeling

Deep learning

RGB color model

Convolutional neural networks

Education and training

Statistical modeling

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