Title: Distracted driving behaviour recognition based on transfer learning and model fusion

Authors: Guantai Luo; Wanghui Xiao; Xinwei Chen; Jin Tao; Chentao Zhang

Addresses: Fujian (Quanzhou)-HIT Research Institute of Engineering and Technology, Quanzhou, Fujian, China ' Fujian (Quanzhou)-HIT Research Institute of Engineering and Technology, Quanzhou, Fujian, China ' Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, Fujian, China ' Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, Fujian, China ' Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, Fujian, China

Abstract: In the recognition of distracted driving behaviour, traditional manual feature extraction is subjective and complex; single deep convolutional network also has problems such as insufficient generalisation performance and stability. To solve the above problems, this paper proposes a distracted driving behaviour recognition method based on transfer learning and model fusion. First, based on the transfer learning method, the deep convolutional neural network models ResNet18 and ResNet34 are used to extract the features of some images, respectively. Furthermore, the pre-trained model is fine-tuned to obtain four deep convolutional neural network models. Finally, the four network models are fused by stacking method, using 5-fold cross-validation method to reduce over-fitting. Experimental results show that the recognition accuracy of distracted driving behaviour after model fusion reaches 95.47%. The fusion model has higher model generalisation performance and recognition accuracy, which can provide certain technical support for the research of distracted driving behaviour recognition.

Keywords: deep learning; transfer learning; model fusion; pattern recognition; distracted driving behaviour.

DOI: 10.1504/IJWMC.2023.130405

International Journal of Wireless and Mobile Computing, 2023 Vol.24 No.2, pp.159 - 168

Received: 13 Oct 2021
Received in revised form: 12 Jun 2022
Accepted: 19 Jul 2022
Published online: 19 Apr 2023 *

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