Driver Activity Recognition for Intelligent Vehicles: A Deep Learning Approach
文献类型：期刊论文
| 作者 | Xing, Yang1; Lv, Chen1; Wang, Huaji2; Cao, Dongpu3; Velenis, Efstathios4; Wang, Fei-Yue5
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| 刊名 | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
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| 出版日期 | 2019-06-01 |
| 卷号 | 68期号:6页码:5379-5390 |
| ISSN号 | 0018-9545 |
| 关键词 | Driver behavior driver distraction convolutional neural network transfer learning |
| DOI | 10.1109/TVT.2019.2908425 |
| 通讯作者 | Lv, Chen(lyuchen@ntu.edu.sg) ; Cao, Dongpu(dongpu.cao@uwaterloo.ca) |
| 英文摘要 | Driver 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 paper. 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 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 analyzed and discussed. |
| WOS关键词 | BEHAVIOR |
| 资助项目 | Young Elite Scientist Sponsorship Program by CAST[2017QNRC001] ; SUG-NAP of Nanyang Technological University, Singapore[M4082268.050] |
| WOS研究方向 | Engineering ; Telecommunications ; Transportation |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000472563200016 |
| 资助机构 | Young Elite Scientist Sponsorship Program by CAST ; SUG-NAP of Nanyang Technological University, Singapore |
| 源URL | [http://ir.ia.ac.cn/handle/173211/26048]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Lv, Chen; Cao, Dongpu |
| 作者单位 | 1.Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore 639798, Singapore 2.AVL Powertrain UK Ltd, Coventry CV4 7EZ, W Midlands, England 3.Univ Waterloo, Dept Mech & Mechatronics Engn, Waterloo, ON N2L 3G1, Canada 4.Cranfield Univ, Adv Vehicle Engn Ctr, Bedford MK43 0AL, England 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xing, Yang,Lv, Chen,Wang, Huaji,et al. Driver Activity Recognition for Intelligent Vehicles: A Deep Learning Approach[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2019,68(6):5379-5390. |
| APA | Xing, Yang,Lv, Chen,Wang, Huaji,Cao, Dongpu,Velenis, Efstathios,&Wang, Fei-Yue.(2019).Driver Activity Recognition for Intelligent Vehicles: A Deep Learning Approach.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,68(6),5379-5390. |
| MLA | Xing, Yang,et al."Driver Activity Recognition for Intelligent Vehicles: A Deep Learning Approach".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 68.6(2019):5379-5390. |
入库方式： OAI收割
来源：自动化研究所
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