传统的基于Radon变换的车牌倾斜校正算法所需的存储空间和运算量大,无法满足车牌检测实时性的要求。为此,研究了在LabVIEW环境下应用改进的Radon变换实现汽车牌照倾斜校正问题,提高了系统的实时性。在LabVIEW环境下动态链接库DLL(Dynam ...传统的基于Radon变换的车牌倾斜校正算法所需的存储空间和运算量大,无法满足车牌检测实时性的要求。为此,研究了在LabVIEW环境下应用改进的Radon变换实现汽车牌照倾斜校正问题,提高了系统的实时性。在LabVIEW环境下动态链接库DLL(Dynam ic L ink L ibrary)工作原理的基础上,通过将角度范围限制在-5°^+5°之间,对已有的算法进行改进,借助LabVIEW高级语言编程语言接口功能,简单方便地实现了车牌的倾斜校正。DLL的引入避免了LabVIEW图形化语言大量繁杂的连线,使得系统结构清晰明了。结果表明,本算法能够更快速、准确地实现车牌的倾斜校正。大小为534×178的车牌倾斜校正的时间小于6.7 s,绝对误差小于0.2°。展开更多
An efficient vehicle detection approach is proposed for traffic surveillance images, which is based on information fusion of vehicle symmetrical contour and license plate position. The vertical symmetry axis of the ve...An efficient vehicle detection approach is proposed for traffic surveillance images, which is based on information fusion of vehicle symmetrical contour and license plate position. The vertical symmetry axis of the vehicle contour in an image is. first detected, and then the vertical and the horizontal symmetry axes of the license plate are detected using the symmetry axis of the vehicle contour as a reference. The vehicle location in an image is determined using license plate symmetry axes and the vertical and the horizontal projection maps of the vehicle edge image. A dataset consisting of 450 images （15 classes of vehicles） is used to test the proposed method. The experimental results indicate that compared with the vehicle contour-based, the license plate location-based, the vehicle texture-based and the Gabor feature-based methods, the proposed method is the best with a detection accuracy of 90.7% and an elapsed time of 125 ms.展开更多
文摘传统的基于Radon变换的车牌倾斜校正算法所需的存储空间和运算量大,无法满足车牌检测实时性的要求。为此,研究了在LabVIEW环境下应用改进的Radon变换实现汽车牌照倾斜校正问题,提高了系统的实时性。在LabVIEW环境下动态链接库DLL(Dynam ic L ink L ibrary)工作原理的基础上,通过将角度范围限制在-5°^+5°之间,对已有的算法进行改进,借助LabVIEW高级语言编程语言接口功能,简单方便地实现了车牌的倾斜校正。DLL的引入避免了LabVIEW图形化语言大量繁杂的连线,使得系统结构清晰明了。结果表明,本算法能够更快速、准确地实现车牌的倾斜校正。大小为534×178的车牌倾斜校正的时间小于6.7 s,绝对误差小于0.2°。
基金The National Natural Science Foundation of China(No. 40804015,61101163)
文摘An efficient vehicle detection approach is proposed for traffic surveillance images, which is based on information fusion of vehicle symmetrical contour and license plate position. The vertical symmetry axis of the vehicle contour in an image is. first detected, and then the vertical and the horizontal symmetry axes of the license plate are detected using the symmetry axis of the vehicle contour as a reference. The vehicle location in an image is determined using license plate symmetry axes and the vertical and the horizontal projection maps of the vehicle edge image. A dataset consisting of 450 images （15 classes of vehicles） is used to test the proposed method. The experimental results indicate that compared with the vehicle contour-based, the license plate location-based, the vehicle texture-based and the Gabor feature-based methods, the proposed method is the best with a detection accuracy of 90.7% and an elapsed time of 125 ms.
