基于最近邻链的车牌检测算法

苗立刚

doi:10.3724/SP.J.1004.2011.01272

基于最近邻链的车牌检测算法

doi: 10.3724/SP.J.1004.2011.01272

苗立刚^1,2

1.
中国科学院研究生院计算与通信工程学院北京 100049;
2.
东北大学秦皇岛分校电子信息系秦皇岛 066004

计量
- 文章访问数: 2229
- HTML全文浏览量: 39
- PDF下载量: 1143
- 被引次数: 0
出版历程
- 收稿日期: 2011-01-13
- 修回日期: 2011-03-13
- 刊出日期: 2011-10-20

License Plate Detection Algorithm Based on Nearest Neighbor Chains

MIAO Li-Gang^1,2

1.
College of Computing Communication Engineering, Graduate University of the Chinese Academy of Sciences, Beijing 100049;
2.
Department of Electronics Information, Northeastern University at Qinhuangdao, Qinhuangdao 066004

摘要

摘要: 根据车牌字符的几何特征和空间排列规则,提出了一种基于最近邻链的自适应车牌检测算法. 首先,采用自适应阈值分割算法消除光照变化的影响,并采用连通体分析方法消除部分干扰目标; 其次,根据车牌字符连通体的区域特征,将宽度和高度都相近的连通体构造为最近邻连通体对, 并将最近邻对连接为最近邻链,从而检测出所有可能的车牌区域; 最后,利用两组不同长度的方波模板分别对车牌的水平和竖直投影进行匹配, 它能够验证候选车牌区域的有效性,并求解所有车牌字符的最佳切分位置. 实验表明,该算法能够自适应地处理光照不均匀、尺度变化、透视失真、背景干扰以及质量退化等因素的影响, 可以有效地检测出复杂背景中的车牌区域.
- 车牌检测 /
- 字符分割 /
- 最近邻链 /
- 连通体分析 /
- 变长模板匹配
Abstract: This paper proposes a nearest neighbor chain based method for adaptive license plate (LP) detection according to the geometrical characteristics and spatial arrangement of license plate characters. Firstly, image is segmented with adaptive binarization method to avoid the problem created by nonuniform illumination, and some undesired image areas are removed by limiting the range of region properties of connected components. Secondly, nearest neighbor pairs are constructed according to region properties of connected components of LP characters, and they are merged into nearest neighbor chains, thus, to detect all the candidate license plate regions. Finally, two variable-length square templates are designed to match horizontal and vertical projections of LP image gradients. It can verify all the candidate LP regions and determine the boundaries of all LP characters simultaneously. Experiments show that this method can deal with nonuniform illumination, LP size change, rotation, background interference, quality degradation and so on, and that it has achieved desired detection result for various types of LP images in complex scenes.
- License plate detection /
- character segmentation /
- nearest neighbor chain /
- connected component analysis /
- variable-length template matching

HTML全文

参考文献(1)

[1]

Anagnostopoulos C E, Anagnostopoulos I E, Psoroulas I D, Loumos V, Kayafas E. License plate recognition from still images and video sequences: a survey. IEEE Transactions on Intelligent Transportation Systems, 2008, 9(3): 377-391[2] Jiao J B, Ye Q X, Huang Q M. A configurable method for multi-style license plate recognition. Pattern Recognition, 2009, 42(3): 358-369[3] Bai H L, Liu C P. A hybrid license plate extraction method based on edge statistics and morphology. In: Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK: IEEE, 2004. 831-834[4] Zheng D, Zhao Y, Wang J. An efficient method of license plate location. Pattern Recognition Letters, 2005, 26(15): 2431-2438[5] Shapiro V, Gluhchev G, Dimov D. Towards a multinational car license plate recognition system. Machine Vision and Applications, 2006, 17(3): 173-183[6] Shi X, Zhao W, Shen Y. Automatic license plate recognition system based on color image processing. In: Proceedings of the International Conference on Computational Science and Its Applications. Berlin, Germany: Springer-Verlag, 2005. 1159-1168[7] Wang F, Man L, Wang B P, Xiao Y J, Pan W, Lu X C. Fuzzy-based algorithm for color recognition of license plates. Pattern Recognition Letters, 2008, 29(7): 1007-1020[8] Jia W J, Zhang H F, He X J. Region-based license plate detection. Journal of Network and Computer Applications, 2007, 30(4): 1324-1333[9] Chang S L, Chen L S, Chung Y C, Chen S W. Automatic license plate recognition. IEEE Transactions on Intelligent Transportation Systems, 2004, 5(1): 42-53[10] Kim K I, Jung K, Kim J H. Color texture-based object detection: an application to license plate localization. In: Proceedings of the 1st International Workshop on Pattern Recognition with Support Vector Machines. London, UK: Springer-Verlag, 2002. 293-309[11] Cui D, Gu D B, Cai H, Sun J X. License plate detection algorithm based on gentle AdaBoost algorithm with a cascade structure. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics. Guilin, China: IEEE, 2009. 1962-1966[12] Arth C, Limberger F, Bischof L. Real-time license plate recognition on an embedded DSP-platform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, USA: IEEE, 2007. 1-8[13] Zhang H, Jia W, He X, Wu Q. Learning-based license plate detection using global and local features. In: Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong, China: IEEE, 2006. 1102-1105[14] Kahraman F, Kurt B, Gokmen M. License plate character segmentation based on the Gabor transform and vector quantization. In: Proceedings of the 18th International Symposium on Computer and Information Sciences. Antalya, Turkey: Springer, 2003. 381-388[15] Abolghasemi V, Ahmadyfard A. An edge-based color-aided method for license plate detection. Image and Vision Computing, 2009, 27(8): 1134-1142[16] Liu Wan-Jun, Jiang Qing-Ling, Zhang Chuang. A novel approach for vehicle license plate locating based on CNN color image edge detection. Acta Automatica Sinica, 2009, 35(12): 1503-1512(刘万军, 姜庆玲, 张闯. 基于CNN彩色图像边缘检测的车牌定位方法. 自动化学报, 2009, 35(12): 1503-1512)[17] Niblack W. An Introduction to Digital Image Processing. Englewood Cliffs: Prentice-Hall, 1986. 115-116[18] Hashizume A, Yeh P S, Rosenfeld A. A method of detecting the orientation of aligned components. Pattern Recognition Letters, 1986, 4: 125-132[19] Lu Y, Tan C L. A nearest-neighbor chain based approach to skew estimation in document images. Pattern Recognition Letters, 2003, 24(14): 2315-2323

施引文献

资源附件(0)

访问统计