巡邏車輛之自動車牌辨識系統

在車輛相關的犯罪調查中車牌已被認定為最重要的資訊，例如：贓車查緝等。以往傳統的調查案件必須透過調查員(警察)手動輸入可疑車輛的車牌號碼至贓車系統，此步驟不僅耗費人力而且制式化的動作使人繁瑣乏味。本研究考量行車紀錄器的錄影功能，以及巡邏車(警車)取締贓車的資訊，提出一套「巡邏車輛之自動車牌辨識系統」，主要是根據巡邏車針對路邊停車之車輛進行自動化的車牌辨識，本系統分為硬體配置及軟體開發兩個部分，並針對這兩個部分進行詳細的說明。技術方面包含：車牌定位、車牌校正、字元切割和字元辨識，根據這些技術本系統在車牌定位和字元辨識上，不管是車牌偵測率和字元辨識率皆可達90%以上。總而言之，本系統可以結合無線通訊技術查詢車輛的相關資訊，並且協助關於車輛的犯罪案件進行調查。

關鍵字

字元辨識；影像處理；車牌辨識；樣板比對

並列摘要

License plates are considered the first important information for vehicle-related crime investigation (e.g., vehicle theft, etc.). Conventional investigation requires the investigator (policeman) to manually enter the license plate number for suspicious vehicles which remains tedious and labor-intensive. The objective of this study was to develop an Automatic license plate recognition system for patrolling vehicles. We explored the idea to provide an automatic system by installing a surveillance camera (e.g., a vehicle video recorder) on a patrolling vehicle (e.g., police car). The system can be described in two phases, namely hardware configuration and software development. Technical approaches included: License Plate Localization, License Plate Correction, Character Segmentation, and Character Recognition. Overall, our system could achieve the license plate localization and character recognition of over 90%. In summary, our system could be incorporated in an integrated system with wireless communication for querying the vehicles’ information to assist the vehicle-related crime investigation.

並列關鍵字

character recognition ； image processing ； license plate recognition ； template matching

參考文獻

[1] P. Rattanathammawat and T. H. Chalidabhongse, “A Car Plate Detector using Edge Information,” Proceeding of the IEEE International Symposium on Communications and Information Technologies (ISCIT), pp. 1039-1043, 2006.

[5] W. Mei and W. G. Hong, “Method of Vehicle License Plate Correction Based on Characters Projection Minimum Distance,” Computer Engineering, vol. 34, no. 6, pp. 216-218, March 2008.

[6] J. M. Guo and Y. F. Liu, “License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques,” IEEE Transactions on Vehicular Technology, vol. 57, no. 3, pp. 1417-1424, May 2008.

[7] X. Zhang, X. Liu and H. Jiang, “A Hybrid Approach to License Plate Segmentation under Complex Conditions,” Third International Conference on Natural Computation (ICNC), pp. 68-73, August 2007.

[10] P. Hidayatullah, N. Syakrani, I. Suhartini and W. Muhlis, “Optical Character Recognition Improvement for License Plate Recognition in Indonesia,” UKSim-AMSS 6th European Modelling Symposium on Computer Modeling and Simulation (EMS), pp. 249-254, November 2012.

國際替代計量

巡邏車輛之自動車牌辨識系統

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