Real-Time Anti-Interference Location of Vehicle License Plates Using High-Definition Video

Considering the fact that High Definition (HD) becomes an important trend in road surveillance video, this paper studies vehicle location and license plate location methods in HD surveillance video. While license plate reading may obviously benefit from high definition technology, higher resolution also increases the computational load of graphical analysis and background interference. Most known approaches to license plate location are not suited to high definition imagery. In this article, a real-time method of license plate location over high-definition surveillance video is discussed, and a reasonable approach to consider HD is proposed. As license plates are affixed on vehicles, a prior vehicle detection step significantly enhances the robustness of license plate detection. It is shown that the frontal area of the vehicle can be located using an AdaBoost cascade classifier. Following this classification step, the region of Vehicle License Plates (VLPs) can be located based on fixed color and texture features of license plate characters and background in HSV color space. This paper also presents extensive experiments using thousands of real video sequences to verify the proposed method.

[1]  L. Spaanenburg,et al.  License plate recognition using DTCNNs , 1998, 1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359).

[2]  Geoffrey D. Sullivan,et al.  Model-based vehicle detection and classification using orthographic approximations , 1997, Image Vis. Comput..

[3]  S Draghici,et al.  A NEURAL NETWORK BASED ARTIFICIAL VISION SYSTEM FOR LICENSE PLATE RECOGNITION , 1997 .

[4]  Keiichi Yamada,et al.  Robust license-plate recognition method for passing vehicles under outside environment , 2000, IEEE Trans. Veh. Technol..

[5]  A. Pece,et al.  Tracking without Feature Detection , 2007 .

[6]  Dieter Koller,et al.  Moving Object Recognition and Classification based on Recursive Shape Parameter Estimation , 1993, CVPR 1993.

[7]  Jitendra Malik,et al.  Traffic Surveillance And Detection Technology Development: New Traffic Sensor Technology Final Report , 1997 .

[8]  Lambert Spaanenburg,et al.  Car license plate recognition with neural networks and fuzzy logic , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Jitendra Malik,et al.  A real-time computer vision system for measuring traffic parameters , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Sorin Draghici,et al.  A Neural Network Based Artificial Vision System for Licence Plate Recognition , 1997, Int. J. Neural Syst..

[11]  Hang Joon Kim,et al.  A recognition of vehicle license plate using a genetic algorithm based segmentation , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[12]  E.D. Di Claudio,et al.  Car plate recognition by neural networks and image processing , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[13]  Dashan Gao,et al.  Car license plates detection from complex scene , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[14]  Bernt Schiele,et al.  Model-free tracking of cars and people based on color regions , 2006, Image Vis. Comput..

[15]  Sung-Il Chien,et al.  Automatic car license plate extraction using modified generalized symmetry transform and image warping , 2001, ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No.01TH8570).

[16]  C. Hilario,et al.  Model based vehicle detection for intelligent vehicles , 2004, IEEE Intelligent Vehicles Symposium, 2004.