Driver behavior recognition based on attention module and bilinear fusion network

Chenkai Ma; Hao Wang; Jianing Li

doi:10.1117/12.2641412

4 August 2022 Driver behavior recognition based on attention module and bilinear fusion network

Chenkai Ma, Hao Wang, Jianing Li

Proceedings Volume 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022); 123061O (2022) https://doi.org/10.1117/12.2641412
Event: Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 2022, Changchun, China

Abstract

Due to the small differences in driver distraction actions and the high similarity of some actions, in this paper we propose a distracted driver behavior recognition method (BACNN) based on convolutional neural network (CNN) using bilinear fusion network and combining attention mechanism with current mainstream algorithms of deep learning. In this paper, we use a driver dataset from State Farm for testing, and use 75% of this dataset for training and 25% for testing. The driver behavior pictures in the dataset are extracted using our specific convolutional neural network model for feature extraction and classified using a fully connected layer. Experiments demonstrate that this method has better recognition results compared to single-model network extracted features.

Citation Download Citation

Chenkai Ma, Hao Wang, and Jianing Li "Driver behavior recognition based on attention module and bilinear fusion network", Proc. SPIE 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 123061O (4 August 2022); https://doi.org/10.1117/12.2641412

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KEYWORDS

RGB color model

Convolutional neural networks

Image fusion

3D modeling

Data modeling

Feature extraction

Roads

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