Real Time Automatic Number Plate Recognition Using Morphological Algorithm

B Satyanarayana, Dhana Raju Valluri, K Raja Sekhar

Abstract


The rising increase of up to date urban and national road networks over the last three decades become known the need of capable monitoring and management of road traffic. Expected techniques for traffic measurements, such as inductive loops, sensors or EM microwave detectors, endure from sober shortcomings, luxurious to install, they demand traffic distraction during installation or maintenance, they are massive and they are unable to notice slow or momentary stop vehicles. On the divergent, systems that are based on video are simple to install, use the existing infrastructure of traffic observation. Currently most reliable method is through the detection of number plates, i.e., automatic number plate recognition (ANPR), which is also branded as automatic license plate recognition (ALPR), or radio frequency transponders.

The first revalent step of information is finding of moving objects in video streams and background subtraction is a very accepted approach for foreground segmentation. Next step is License plate extraction which is an essential stage in license plate recognition for automatic transport system. We are planned for two ways for removal of license plates and comparing it with other existing methods. The Extracted license plates are segmented into particular characters by means of a region-based manner. The recognition scheme unites adaptive iterative thresholding with a template matching algorithm. The method is strong to illumination, character size and thickness, skew and small character breaks. The main reward of this system is its real-time capability and that it does not require any extra sensor input (e.g. from infrared sensors) except a video stream. This system is judged on a huge number of vehicle images and videos. The system is also computationally extremely efficient and it is appropriate for others related image recognition applications. This system has broad choice of applications such as access control, ringing, border patrol, traffic control, finding stolen cars, etc. Furthermore, this technology does not need any fitting on cars, such as transmitter or responder.


References


Hu, Weiming, Tieniu Tan, Liang Wang, and Steve Maybank. "A survey on visual surveillance of object motion and behaviors." Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 34, no. 3 (2004): 334-352.

Buch, Norbert, Sergio A. Velastin, and James Orwell. "A review of computer vision techniques for the analysis of urban traffic." Intelligent Transportation Systems, IEEE Transactions on 12, no. 3 (2011): 920-939.

Zhu, Zhongjie, and Yuer Wang. "A hybrid algorithm for automatic segmentation of slowly moving objects." AEU-International Journal of Electronics and Communications 66, no. 3 (2012): 249-254.

Lipton, Alan J., Hironobu Fujiyoshi, and Raju S. Patil. "Moving target classification and tracking from real-time video." In Applications of Computer Vision, 1998. WACV'98. Proceedings., Fourth IEEE Workshop on, pp. 8-14. IEEE, 1998.

Barron, John L., David J. Fleet, and S. S. Beauchemin. "Performance of optical flow techniques." International journal of computer vision 12, no. 1 (1994): 43-77.

Barnich, Olivier, and Marc Van Droogenbroeck. "ViBe: A universal background subtraction algorithm for video sequences." Image Processing, IEEE Transactions on 20, no. 6 (2011): 1709-1724.

Elgammal, Ahmed, David Harwood, and Larry Davis. "Non-parametric model for background subtraction." In Computer Vision—ECCV 2000, pp. 751-767. Springer Berlin Heidelberg, 2000.

Toyama, Kentaro, John Krumm, Barry Brumitt, and Brian Meyers. "Wallflower: Principles and practice of background maintenance." In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, vol. 1, pp. 255-261. IEEE, 1999.

Panda, Deepak Kumar. "Motion detection, object classification and tracking for visual surveillance application." PhD diss., 2012.

Haritaoglu, Ismail, David Harwood, and Larry S. Davis. "W4: real-time surveillance of people and their activities." Pattern Analysis and Machine Intelligence, IEEE Transactions on 22, no. 8(2000): 809-830.

Antoine Manzanera and Julien C. Richefeu. A new motion detection algorithm based on [Sigma]-[Delta] background estimation. Pattern Recognition Letters, 28(3):320–328, February 2007.

Manuel Vargas. et al.“An Enhanced Background Estimation Algorithm for Vehicle Detection in Urban Traffic Scenes”, IEEE Transactions On Vehicular Technology, Vol. 59, No. 8, October2010.

Stauffer, C., & Grimson, W. (1999). Adaptive background mixture models for real-time tracking.Computer Vision Pattern Recognition, 246–252.

M. Harville, “A framework for high-level feedback to adaptive, per-pixel, mixture-of-Gaussian

Wei Zhang. et al. “Moving vehicles segmentation based on Bayesian framework for Gaussian motion model” Pattern Recognition Letters 27 (2006) 956–967, ELSEVIER

Cucchiara, R., Piccardi, M. (1999). Vehicle detection under day and night illumination. In Proceedings of 3rd international ICSC symposium on intelligent industrial automation.

Z. Zhu and G. Xu, “VISATRAM: A real-time vision system for automatic traffic monitoring,” Image Vis. Comput., vol. 18, no. 10, pp. 781–794, Jul. 2000.

D.Panda and S.Mehar “Robust Real-Time Object Tracking Under Background Clutter”, International Conference on Image Information Processing (ICIIP 2011) 2011.

Nicholas A., Iphigenia K & Chris T. Kiranoudis. A background subtraction algorithm for detecting and tracking vehicles. ELSEVIER Expert Systems with Applications 38 (2011) 1619– 1631

K.K. Kim. et al. "Learning-based approach for license plate recognition," IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing, vol. 2, pp. 614- 623.2000.

Aditya Acharya and Sukadev Meher “Region Adaptive Unsharp Masking based DCT Interpolation for Efficient Video Intra Frame Up-Sampling.” IJCA Special Issue on International Conference ICEDSP(3):24-28, February 2013.

Hongwei Ying, Jiatao Song, Xiaobo Ren” Character Segmentation for License Plate by the Separator Symbol's Frame of Reference” 2010 International Conference on Information,Networking and Automation (ICINA).

“Mathematical morphology” from Wikipedia, the free encyclopedia

http://en.wikipedia.org/wiki/Mathematical_morphology#Closing

Shuang Qiaol , Yan Zhul , Xiufen Li l , Taihui Liu2 ,3, Baoxue Zhangl “Research of improving the accuracy of license plate character segmentation” 2010 Fifth International Conference on Frontier of Computer Science and Technology.

Deng Hongyao, Song Xiuli “License Plate Characters Segmentation Using Projection and Template Matching”, 2009 International Conference on Information Technology and Computer Science.

Yungang Zhang, Changshui Zhang, “A New Algorithm for Character Segmentation of License Plate”: Dept. of Automation, Tsinghua University, The Institute of Information Processing Beijing, Chin

Huang, Yo-Ping, Chien-Hung Chen, Yueh-Tsun Chang, and Frode Eika Sandnes. "An intelligent strategy for checking the annual inspection status of motorcycles based on license plate recognition." Expert Systems with Applications 36, no. 5 (2009): 9260-9267.

Shapiro, Vladimir, Georgi Gluhchev, and Dimo Dimov. "Towards a multinational car license plate recognition system." Machine Vision and Applications 17, no. 3 (2006): 173-183.

Efford, Nick. "Digital Image Processing: A Practical Introduction Using Java. 2000."

Gonzalez, Rafael C., Richard E. Woods, and Steven L. Eddins. Digital image processing using MATLAB. Vol. 2. Tennessee.,Gatesmark Publishing, 2011


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