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
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.
霍星, 檀结庆, 赵峰, 景永俊, 邵堃. 基于多识别区域融合的机动车驾驶员检测框架*[J]. 模式识别与人工智能, 2018, 31(3): 283-292.
HUO Xing, TAN Jieqing, ZHAO Feng, JING Yongjun, SHAO Kun. Motor Vehicle Driver Detection Framework Based on Multi-detected Region Fusion. , 2018, 31(3): 283-292.
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2018, Vol. 31 
