摘要
针对管制车型在城市交通监控难等问题,研究一种基于双相机协同的管制车型自动抓拍系统。首先依据管制车型侧面信息熵大而便于类内目标精细识别的特点,优选目标车辆的侧面图像为特征载体,采用卷积神经网络对不同管制车型侧面进行识别;其次建立相机间坐标映射模型,采用BP神经网络算法对目标车辆面部进行定位,有效简化相机协同标定工序,对目标车辆面部进行快速定位;最后基于目标车辆的面部图像对车牌进行识别。实验结果表明,该方法对管制车型的检测精度达到89.94%,定位面部区域与ground truth的纵向高度交并比达到0.912,系统整体性能达到较高精度。
Aiming at the difficult problem of urban traffic monitoring for regulated vehicles, an automatic capture system for regulated vehicles based on dual camera collaboration is studied. Firstly, according to the characteristics of the large side information entropy of the control model, which is easy to identify the inter-class target, the target side image is priorly selected as the feature carrier, and the convolutional neural network is used to identify the side of different models. Secondly, the coordinate mapping model between cameras is established. The BP neural network algorithm is used to locate the target vehicle’s face, which effectively simplifies the camera collaborative calibration process and quickly locates the target vehicle’s face. Finally, the license plate is identified based on the target vehicle’s facial image. The experimental results show that the detection accuracy of the proposed model is 89.94%, and the vertical height of the positioning face area and the ground truth is 0.912. The overall performance of the system reaches a high precision.
作者
王喜龙
张伟伟
吴训成
WANG Xi-long;ZHANG Wei-wei;WU Xun-cheng(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《计算机与现代化》
2019年第3期23-27,共5页
Computer and Modernization
基金
国家自然科学基金资助项目(51675324
51575169)
国家重点研发计划项目(2016YFC0800702-1)
上海高校青年教师培养资助计划项目(ZZGCD15102)
上海高校教师产学研计划项目(A3-0100-17-SDJH337)
上海市青年科技英才扬帆计划项目(18YF1409400)
关键词
车辆侧面特征
双相机协同
车型识别
vehicle’s side features
dual camera collaboration
vehicle type identification
