Driver activity recognition for intelligent vehicles: a deep learning approach

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dc.contributor.authorXing, Yang
dc.contributor.authorLv, Chen
dc.contributor.authorWang, Huaji
dc.contributor.authorCao, Dongpu
dc.contributor.authorVelenis, Efstathios
dc.contributor.authorWang, Fei-Yue
dc.date.accessioned2019-05-02T19:16:45Z
dc.date.available2019-05-02T19:16:45Z
dc.date.issued2019-04-01
dc.description.abstractDriver decisions and behaviors are essential factors that can affect the driving safety. To understand the driver behaviors, a driver activities recognition system is designed based on the deep convolutional neural networks (CNN) in this study. Specifically, seven common driving activities are identified, which are the normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle radio device, texting, and answering the mobile phone, respectively. Among these activities, the first four are regarded as normal driving tasks, while the rest three are classified into the distraction group. The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection. The raw images are segmented using the Gaussian mixture model (GMM) to extract the driver body from the background before training the behavior recognition CNN model. To reduce the training cost, transfer learning method is applied to fine tune the pre-trained CNN models. Three different pre-trained CNN models, namely, AlexNet, GoogLeNet, and ResNet50 are adopted and evaluated. The detection results for the seven tasks achieved an average of 81.6% accuracy using the AlexNet, 78.6% and 74.9% accuracy using the GoogLeNet and ResNet50, respectively. Then, the CNN models are trained for the binary classification task and identify whether the driver is being distracted or not. The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analysed and discussed.en_UK
dc.identifier.citationYang Xing, Chen Lv, Huaji Wang et al., (2019) Driver activity recognition for intelligent vehicles: a deep learning approach. IEEE Transactions on Vehicular Technology, Volume 68, Issue 6, June 2019, pp.5379 - 5390en_UK
dc.identifier.cris23382100
dc.identifier.issn0018-9545
dc.identifier.urihttps:doi.org/ 10.1109/TVT.2019.2908425
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14128
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectdriver behaviouren_UK
dc.subjectdriver distractionen_UK
dc.subjectconvolutional neural networken_UK
dc.subjecttransfer learningen_UK
dc.titleDriver activity recognition for intelligent vehicles: a deep learning approachen_UK
dc.typeArticleen_UK

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