Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88081
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Huang, C | - |
dc.creator | Wang, XC | - |
dc.creator | Cao, JN | - |
dc.creator | Wang, SH | - |
dc.creator | Zhang, Y | - |
dc.date.accessioned | 2020-09-18T02:12:34Z | - |
dc.date.available | 2020-09-18T02:12:34Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/88081 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.rights | 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 | en_US |
dc.subject | Feature extraction | en_US |
dc.subject | Vehicles | en_US |
dc.subject | Accidents | en_US |
dc.subject | Training | en_US |
dc.subject | Safety | en_US |
dc.subject | Cameras | en_US |
dc.subject | Roads | en_US |
dc.subject | Distracted drivers | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Transfer learning | en_US |
dc.subject | Fusion model | en_US |
dc.title | HCF : a hybrid CNN framework for behavior detection of distracted drivers | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 109335 | - |
dc.identifier.epage | 109349 | - |
dc.identifier.volume | 8 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3001159 | - |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2020, v. 8, p. 109335-109349 | - |
dcterms.isPartOf | IEEE access | - |
dcterms.issued | 2020 | - |
dc.identifier.isi | WOS:000549854800003 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.description.validate | 202009 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
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 |
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