Selected aspects of digital image processing applications in ITS

• Autorzy: Mrówka P. , Olejniczak M.
• Języki publikacji: EN

Abstrakty

EN

Digital image processing algorithms are commonly applied in Intelligent Transport Systems (ITS). Their effective operation is conditioned on the high robustness to real-life image distortions and the computational complexity suitable for implementation on a non expensive industrial computer. The paper presents three original image analysis methods designed for the ITS, with special attention paid on aforementioned conditions. Colour image parametrization method for the traffic light state classifier was described. The algorithm utilizes CIELAB colour space properties. The method of vehicle edges parametrization for the make and model classifier was presented. The proposed representation relies on thresholded coefficients of gradient magnitude approximation in low dimensional space. The paper presents also the method of image characteristic features detection for the licence plates localization task. The detection is performed by means of appropriately designed filters with low computational complexity.

Słowa kluczowe

EN

digital image processing; image parametrization; traffic light classification; colour recognition; model recognition

PL

cyfrowe przetwarzanie obrazu; parametryzacja obrazu; klasyfikacja sygnalizacji świetlnej; rozpoznawanie koloru; rozpoznawanie modelu

• Wydawca: Polskie Stowarzyszenie Telematyki Transportu
• Czasopismo: Archives of Transport System Telematics
• Rocznik: 2013
• Tom: Vol. 6, iss. 3
• Strony: 23--26
• Opis fizyczny: Bibliogr. 8 poz.

Twórcy

autor

Mrówka P.

• Neurosoft Sp. z o. o., Robotnicza 72, 53-608 Wrocław, Poland

autor

Olejniczak M.

• Neurosoft Sp. z o. o., Robotnicza 72, 53-608 Wrocław, Poland

Bibliografia

• [1] Chiang , C.-C., Ho, M.-C., Liao , H.-S., Pratama , A., Syu, W.-C.: Detecting and Recognizing Traffic Lights by Genetic Approximate Ellipse Detection and Spatial Texture Layouts. Intern. Journ. of Innovative Computing, Information and Control, Vol. 7, No. 12 (2011)
• [2] Wang , C., Jin, T., Yang , M., Wang , B.: Robust and Real-Time Traffic Lights Recognition in Complex Urban Environments. Intern. Journ. of Computational Intelligence Systems, Vol. 4, No. 6 (2011)
• [3] Gevers , T., Smeulders , A. W. M.: Color-based object recognition. Pattern Recognition 32 (1999)
• [4] Fairchild , M. D.: Color Appearance Models. John Wiley and Sons (2005)
• [5] Prokaj , J., Medioni , G.: 3-D Model Based Vehicle Recognition. Workshop on Applications of Computer Vision (2009)
• [6] Hsieh , J.-W., Yu, S.-H., Chen, Y.-S., Hu, W.-F.: Automatic Traffic Surveillance System for Vehicle Tracking and Classification. IEEE Trans. on Intelligent Transportation Systems, Vol. 7, No. 2 (2006)
• [7] Kato , T., Ninomiya, Y., Masaki , I.: Preceding Vehicle Recognition Based on Learning From Sample Images. IEEE Trans. on Intelligent Transportation Systems, Vol. 3, No. 4 (2002)
• [8] Psyllos , A., Anagnostopoulos , C. N., Kayafas ,E.: Vehicle model recognition from frontal view image measurements. Computer Standards & Interfaces, Vol. 33, Issue 2 (2011)