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| Motor Vehicle Driver Detection Framework Based on Multi-detected Region Fusion |
| HUO Xing1, TAN Jieqing2, ZHAO Feng2, JING Yongjun3, SHAO Kun3 |
1.School of Mathematics, Hefei University of Technology, Hefei 230009
2.Tiansheng Science and Technology Corporation, Hefei 230094
3.School of Computer and Information, Hefei University of Technology, Hefei 230009 |
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Abstract Features of motor vehicle driver are difficult to be acquired in motor vehicle driver detection due to the various illumination conditions, image noise and complex backgrounds. Therefore, an accurate driver location detection framework based on multi-detected region fusion is proposed in this paper to promote the driver identification rate. In the detection process, license plate location is obtained firstly by the algorithm based on image gradient features. Then, the window area of the vehicle is determined by an adaptive method. Finally, the multi-detected region fusion strategy is introduced to obtain the accurate driver region. Experiments on the testing image library verify the high identification rate of the proposed algorithm.
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Received: 15 May 2017
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| Fund:Supported by National Natural Science Foundation of China(No.61502136,61572167), International Cooperation Project of Ministry of Science and Technology(No.2015DFA11450), Science and Technology for Police Project of Anhui Province(No.1604d0 802018), Science and Technology Planning Project of Guangdong Province(No.2016B010108002) |
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Corresponding Authors:
SHAO Kun, Ph.D., associate professor. His research inte-rests include trust evaluation model, requirement engineering and software theory.
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| About author:: HUO Xing, Ph.D., associate professor. Her research interests include machine lear-ning and image processing.TAN Jieqing, Ph.D., professor. His research interests include CAGD and image processing.ZHAO Feng, master, senior engineer. His research interests include signal and information processing, ITS and traffic safety.JING Yongjun, Ph.D. candidate, senior engineer. His research interests include software engineering and machine learning. |
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2018, Vol. 31 
