Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88081
Title: | HCF : a hybrid CNN framework for behavior detection of distracted drivers | Authors: | Huang, C Wang, XC Cao, JN Wang, SH Zhang, Y |
Issue Date: | 2020 | Source: | IEEE access, 2020, v. 8, p. 109335-109349 | Abstract: | Distracted driving causes a large number of traffic accident fatalities and is becoming an increasingly important issue in recent research on traffic safety. Gesture patterns are less distinguishable in vehicles due to in-vehicle physical constraints and body occlusions from the drivers. However, by capitalizing on modern camera technology, convolutional neural network (CNN) can be used for visual analysis. In this paper, we present a hybrid CNN framework (HCF) to detect the behaviors of distracted drivers by using deep learning to process image features. To improve the accuracy of the driving activity detection system, we first apply a cooperative pretrained model that combines ResNet50, Inception V3 and Xception to extract driver behavior features based on transfer learning. Second, because the features extracted by pretrained models are independent, we concatenate the extracted features to obtain comprehensive information. Finally, we train the fully connected layers of the HCF to filter out anomalies and hand movements associated with non-distracted driving. We apply an improved dropout algorithm to prevent the proposed HCF from overfitting to the training data. During the evaluation, we apply the class activation mapping (CAM) technique to highlight the feature area involving ten tested classes of typical distracted driving behaviors. The experimental results show that the proposed HCF achieves the classification accuracy of 96.74% when detecting distracted driving behaviors, demonstrating that it can potentially help drivers maintain safe driving habits. | Keywords: | Feature extraction Vehicles Accidents Training Safety Cameras Roads Distracted drivers Convolutional neural network Transfer learning Fusion model |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | EISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2020.3001159 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ The following publication Huang, C., Wang, X. C., Cao, J. N, Wang, S. H., & Zhang, Y. (2020). HCF: A hybrid CNN framework for behavior detection of distracted drivers. IEEE access, 8, 109335-109349 is available at https://dx.doi.org/10.1109/ACCESS.2020.3001159 |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Huang_HCF_CNN_Drivers.pdf | 2.78 MB | Adobe PDF | View/Open |
Page views
86
Last Week
6
6
Last month
Citations as of May 5, 2024
Downloads
88
Citations as of May 5, 2024
SCOPUSTM
Citations
63
Citations as of Apr 19, 2024
WEB OF SCIENCETM
Citations
44
Citations as of May 2, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.