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Shadow Removal for Greyscale Video Sequences

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
The paper presents a shadow detection and elimination algorithm designed for greyscale video sequences. The paper proposes: an automatic method for determining the binarization threshold on the basis of the object edge analysis, division of areas identified as potential shadow using a rectangular grid, analyzing the similarities between the current frame and the background model performed in areas and analyzing the potential shadows areas position relative to the position of areas identified as a true object. The algorithm was designed to eliminate shadows casted by people in video surveillance sequences. The obtained results show the usefulness of the proposed solution.
Twórcy
autor
  • AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Automatics and Biomedical Engineering, Laboratory of Biocybernetics
Bibliografia
  • [1] C. Benedek, T. Szirànyi, Study on color space selection for detecting cast shadows in video surveillance, International Journal of Imaging Systems and Technology, Vol. 17, No. 3, pp. 190-201, 2007
  • [2] S. Bi, D. Liang, X. Shen, Q. Wang, Human Cast Shadow Elimination Method Based on Orientation Information Measures, IEEE International Conference on Automation and Logistics, pp. 1567-1571, 2007
  • [3] Candela, ITEA CANDELA project: Content Analysis and Network Delivery Architectures, http:// www.multitel.be/~va/candela, 2005 (last access 30.05.2012)
  • [4] S.Y. Elhabian, K.M. El-Sayed, S.H. Ahmed, Moving object detection in spatial domain using background removal techniques, state-of-art, Recent Patents on Computer Science, Vol. 1, No. 1, pp. 32-54, 2008
  • [5] C. Fredembach, G. Finlayson, Simple shadow removal, In Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), pp. 832-835, 2006
  • [6] i-LIDS - Imagery Library for Intelligent Detection Systems, www.ilids.co.uk, (last access 30.05.2012)
  • [7] J.C.S. Jacques, C.R. Jung, S.R. Musse, Background Subtraction and Shadow Detection in Grayscale Video Sequences, In Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI ’05), IEEE Computer Society, Washington, DC, USA, 2005
  • [8] C.R. Jung, Efficient Background Subtraction and Shadow Removal for Monochromatic Video Sequences, IEEE Transactions on Multimedia, Vol. 11, No. 3, pp. 571-577, April 2009
  • [9] T. Kryjak, Analiza i testowanie algorytmów generacji tła na potrzeby systemu monitoringu przestrzeni publicznej, Automatyka: półrocznik Akademii Górniczo-Hutniczej im. Stanisława Staszica w Krakowie, zeszyt 3, Wydawnictwa AGH, 2011
  • [10] T. Kryjak, M. Komorkiewicz, M. Gorgon, Real-time moving object detection for video surveillance system in FPGA, In The 2011 Conference on Design & Architectures for Signal and Image Processing (DASIP), pp. 209 - 216, 2011
  • [11] A.T. Nghiem, F. Bremond, M. Thonnat, Shadow Removal in Indoor Scenes, Advanced Video and Signal Based Surveillance, AVSS ’08, IEEE Fifth International Conference on Advanced Video Surveillance Systems, pp. 291-298, 1-3 Sept. 2008
  • [12] PETS, (2006) Ninth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance - PETS 2006 Benchmark Data, http://www.cvg.rdg.ac.uk/PETS2006/data.html, (last access 30.05.2012)
  • [13] A. Prati, R. Cucchiara, I. Mikic, M. Trivedi, Analysis and detection of shadows in video stream: a comparative evaluation Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recoginition, 2001
  • [14] R. Qin, S. Liao, Z. Lei, S.Z. Li, Moving Cast Shadow Removal Based on Local Descriptors, In Proceedings of the 20th International Conference on Pattern Recognition (ICPR), pp. 1377-1380, Aug. 2010
  • [15] A. Sanin, C. Sanderson, B.C. Lovell, Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios, In Proceedings of the 20th International Conference on Pattern Recognition (ICPR), pp. 141-144, Aug. 2010
  • [16] P.S. Szczepaniak, R. Tadeusiewicz, The Role of Artificial Intelligence, Knowledge and Wisdom in Automatic Image Understanding, Journal of Applied Computer Science, Vol. 18, No. 1, 2010, pp. 75-85
  • [17] M. Vargas, S.L. Toral, J.M. Milla, F. Barrero, A shadow removal algorithm for vehicle detection based on reflectance ratio and edge density, 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1123-1128, Sept. 2010
  • [18] C.R. Wren, A. Azarbayejani, T. Darrell, A.P. Pentland, Pfinder: real-time tracking of the human body, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 780-785, 1997
  • [19] D. Xu, J. Liu, Z. Liu, X. Tang, Indoor shadow detection for video segmentation, IEEE International Conference on Multimedia and Expo (ICME ’04), Vol. 1, pp. 41-44, June 2004
  • [20] N. Zarawska, N. Schmidt, The use of colour information in background generation and moving object segmentation, Bachelor thesis, AGH University of Science and Technology in Krakow, Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of Automatics, 2011
  • [21] Z. Zhu, X. Lu, An Accurate Shadow Removal Method for Vehicle Tracking, International Conference on Artificial Intelligence and Computational Intelligence (AICI), Vol. 2, pp. 59-62, Oct. 2010
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ed0cd8ed-fefa-42e5-8e18-edda0e505079
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