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Vision-based parking lot occupancy evaluation system using 2D separable discrete wavelet transform

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A simple system for rough estimation of the occupancy of an ad-hoc organized parking lot is presented. A reasonably simple microprocessor hardware with a low resolution monochrome video camera observing the parking lot from the location high above the parking surface is capable of running the proposed 2-D separable discrete wavelet transform (DWT)-based algorithm, reporting the percentage of the observed parking area occupied by cars. A simple calibration is needed – the mask covering all the areas outside the parking lot must be prepared. The proposed system has been tested on the dedicated FPGA-based hardware in real conditions and proven immune to scene and light changes. As it is discussed in the paper, it can be used in low-power wireless sensor networks.
Rocznik
Strony
569--–573
Opis fizyczny
Bibliogr. 15 poz., rys., fot.
Twórcy
  • Department of Microelectronic Systems, Gdańsk University of Technology, 11/12 Narutowicza St., 80-233 Gdańsk, Poland
  • Department of Microelectronic Systems, Gdańsk University of Technology, 11/12 Narutowicza St., 80-233 Gdańsk, Poland
  • Multimedia Systems Department, Gdańsk University of Technology, 11/12 Narutowicza St., 80-233 Gdańsk, Poland
Bibliografia
  • [1] M.Y.I. Idris, Y.Y. Leng, E.M. Tamil, N.M. Noor, and Z. Razak, “Car park system: a review of smart parking system and its technology”, Information Technology J. 8 (2), 101–113 (2009).
  • [2] A. Hampapur, L. Brown, J. Connell, A. Ekin, N. Haas, M. Lu, H. Merkl, and S. Pankanti, “Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking”, IEEE Signal Process. Mag. 22 (2), 38–51 (2005).
  • [3] M. Casares, S. Velipasalar, and A. Pinto, “Light-weight salient foreground detection for embedded smart cameras”, Computer Vision and Image Understanding 114 (11), 1223–1237 (2010).
  • [4] J. Wu, X. Zhang, and J. Zhou, “Vehicle detection in static road images with PCA and wavelet-based classifier”, Proc. IEEE Intelligent Transportation Systems Conf. 1, 740–744 (2001).
  • [5] D.B.L. Bong, K.C. Ting, and K.C. Lai, “Integrated approach in the design of car park”, IAENG Int. J. Comput. Sci. 35 (1), 7–14 (2008).
  • [6] L. Tsai, J. Hsieh, and K. Fan, “Vehicle detection using normalized color and edge map”, IEEE Trans. Image Process. 16 (3), 850–864 (2007).
  • [7] C. Huang and S. Wang, “A hierarchical Bayesian generation framework for vacant parking space detection”, IEEE Trans. Circuits Syst. Video Technol. 20 (12), 1770–1785 (2010).
  • [8] A. Gardel Vicente, I. Bravo Munoz, P.J. Molina, and J.L.L. Galilea, “Embedded vision modules for tracking and counting people”, IEEE Trans. Instrum. Meas. 58 (9), 3004–3011 (2009).
  • [9] S. Szczepański, M. Wójcikowski, B. Pankiewicz, M. Kłosowski, and R. Żaglewski, “FPGA and ASIC implementation of the algorithm for traffic monitoring in urban areas”, Bull. Pol. Ac.: Tech. 59 (2), 137–140 (2011).
  • [10] M. Wójcikowski, R. Żaglewski, B. Pankiewicz, M. Kłosowski, and S. Szczepański, “Hardware-software implementation of a sensor network for city traffic monitoring using the FPGA- and ASIC-based sensor nodes”, J. Signal Processing Systems 71 (1), 57–73 (2013).
  • [11] W. Jendernalik, G. Blakiewicz, A. Handkiewicz, and M. Melosik, “Analogue CMOS ASICS in image processing systems”, Metrol. Meas. Syst. XX (4), 613–622 (2013).
  • [12] S. Hui and S.H. Żak, “Discrete Fourier transform based pattern classifiers”, Bull. Pol. Ac.: Tech. 62 (1), 15–22 (2014).
  • [13] Y. Geng and C.G. Cassandras, “A new ‘smart parking’ system based on resource”, IEEE Tran. Intell. Transp. Syst. 14 (3), 1129–1139 (2013).
  • [14] M. Klosowski, “Hardware accelerated implementation of wavelet transform for machine vision in road traffic monitoring system”, 1st International Conference on Information Technology IT 2008, DOI 10.1109/INFTECH.2008.4621686, 1–4 (2008).
  • [15] M. Kociołek, A. Materka, M. Strzelecki, and P. Szczypiński, “Discrete wavelet transform - derived features for digital image texture analysis”, Proc. Int. Conf. Signals and Electronic Systems 1, 111–116 (2001).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8ba6dded-bfe0-4892-8482-ecc34a3ef7c8
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