FPGA and ASIC implementation of the algorithm for traffic monitoring in urban areas

• Autorzy: Szczepański S. , Wójcikowski M. , Pankiewicz B. , Kłosowski M. , Żaglewski R.
• Języki publikacji: EN

Abstrakty

EN

This paper describes the idea and the implementation of the image detection algorithm, that can be used in integrated sensor networks for environment and traffic monitoring in urban areas. The algorithm is dedicated to the extraction of moving vehicles from real-time camera images for the evaluation of traffic parameters, such as the number of vehicles, their direction of movement and their approximate speed. The authors, apart from the careful selection of particular steps of the algorithm towards hardware implementation, also proposed novel improvements, resulting in increasing the robustness and the efficiency. A single, stationary, monochrome camera is used, simple shadow and highlight elimination is performed. The occlusions are not taken into account, due to placing the camera at a location high above the road. The algorithm is designed and implemented in pipelined hardware, therefore high frame-rate efficiency has been achieved. The algorithm has been implemented and tested in FPGA and ASIC.

Słowa kluczowe

EN

FPGA; ASIC; sensor network

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2011
• Tom: Vol. 59, nr 2
• Strony: 137--140
• Opis fizyczny: Bibliogr. 7 poz., rys., tab.

Twórcy

autor

Szczepański S.

autor

Wójcikowski M.

autor

Pankiewicz B.

autor

Kłosowski M.

autor

Żaglewski R.

• Faculty of Electronics Telecommunication and Informatics, Gdańsk University of Technology, 11/12 G. Narutowicza St., 80-233 Gdańsk, Poland,

Bibliografia

• [1] R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, “Statistic and knowledge-based moving object detection in traffic scenes”, IEEE Proc. Intelligent Transportation Systems 1, 27–32 (2000).
• [2] A. Elgammal, D. Harwood, and L.S. Davis, “Non-parametric model for background subtraction”, Eur. Conf. Computer Vision. II 751–767 (2000).
• [3] D. Duque, H. Santos, and P. Cortez, “Moving object detection unaffected by cast shadows. highlights and ghosts”, Proc. IEEE Int. Conf. Image Processing 1, 413–416 (2005).
• [4] R. Cucchiara, C. Granna. M. Piccardi, A. Prati, and S. Sirotti, “Improving shadow suppression in moving object detection with HSV color information”, Proc. Intelligent. Transportation Syst. 1, 334–339 (2001).
• [5] T.N. Schoepflin and D.J. Dailey, “Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation”, IEEE Trans. Intell. Transp. Syst. 4 (2), 90–98 (2003).
• [6] D. Koller, J. Weber, and J. Malik, “Robust multiple car tracking with occlusion reasoning”, Proc. Eur. Conf. on Computer Vision 1, 189–196 (1994).
• [7] M. Wójcikowski, R. Żaglewski, and B. Pankiewicz, “FPGAbased real-time implementation of detection algorithm for automatic traffic surveillance sensor network”, J. Signal Processing Systems, OnlineFirstTM, (2011).