Outdoor Vacant Parking Space Detector for Improving Mobility in Smart Cities

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8154)


Difficulty faced by drivers in finding a parking space in either car parks or in the street is one of the common problems shared by all the big cities, most of the times leading to traffic congestion and driver frustration. Exploiting the capabilities that Computer Vision offers, an alternative to those ITS commercial solutions for parking space detection that rely on other sensors different from cameras is presented. The system is able to detect vacant spaces and classify them by the type of vehicle that could park in that area. First of all, an approximate inverse perspective transformation is applied for 2D to 3D reconstruction of parking area. In addition, feature analysis based on Pyramid Histogram of Oriented Gradients (PHOG) is carried out on every parking zone within the parking area. Experiments on real scenarios show the excellent capabilities of the proposed system with independence of camera orientation in the context.


vehicle detection video surveillance outdoor parking 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Swedberg, C.: SF Uses Wireless Sensors to Help Manage Parking. RFID Journal (2007)Google Scholar
  2. 2.
    Degerman, P., Pohl, J., Sethson, M.: Hough transform for parking space estimation using long range ultrasonic sensors. SAE Paper. Document Number: 2006-01-0810 (2006)Google Scholar
  3. 3.
    Satonaka, H., Okuda, M., Hayasaka, S., Endo, T., Tanaka, Y., Yoshida, T.: Development of parking space detection using an ultrasonic sensor. In: 13th World Congress on Intelligent Transportation Systems and Services (2006)Google Scholar
  4. 4.
    Jung, H.G., Cho, Y.H., Yoon, P.J., Kim, J.: Integrated side/rear safety system. In: 11th European Automotive Congress (2007)Google Scholar
  5. 5.
    Schanz, A., Spieker, A., Kuhnert, D.: Autonomous parking in subterranean garages: a look at the position estimation. In: IEEE Intelligent Vehicle Symposium, pp. 253–258 (2003)Google Scholar
  6. 6.
    Gorner, S., Rohling, H.: Parking lot detection with 24 GHz radar sensor. In: 3rd International Workshop on Intelligent Transportation (2006)Google Scholar
  7. 7.
    Foresti, G.L., Micheloni, C., Snidaro, L.: Event classification for automatic visual-based surveillance of parking lots. In: 17th International Conference on Pattern Recognition, vol. 3, pp. 314–317 (2004)Google Scholar
  8. 8.
    Wang, X.G., Hanson, A.R.: Parking lot analysis and visualization from aerial images. In: 4th IEEE Workshop Applications of Computer Vision, pp. 36–41 (1998)Google Scholar
  9. 9.
    Lee, C.H., Wen, M.G., Han, C.C., Kou, D.C.: An automatic monitoring approach for unsupervised parking lots in outdoors. In: 39th Annual International Carnahan Conference, pp. 271–274 (2005)Google Scholar
  10. 10.
    Masaki, I.: Machine-vision systems for intelligent transportation systems. In: IEEE Conference on Intelligent Transportation System, vol. 13(6), pp. 24–31 (1998)Google Scholar
  11. 11.
    Dan, N.: Parking management system and method. US Patent, Pub. No.: 20030144890A1 (2003)Google Scholar
  12. 12.
    Pecharromán, A., Sánchez, N., Torres, J., Menéndez, J.M.: Real-Time Incidents Detection in the Highways of the Future. In: 15th Portuguese Conference on Artificial Intelligence, EPIA 2011, Lisbon, pp. 108–121 (2011)Google Scholar
  13. 13.
    Chen, L., Hsieh, J., Lai, W., Wu, C., Chen, S.: Vision-Based Vehicle Surveillance and Parking Lot Management Using Multiple Cameras. In: 6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Washington, DC, pp. 631–634 (2010)Google Scholar
  14. 14.
    True, N.: Vacant Parking Space Detection in Static Images, Projects in Vision & Learning, University of California (2007)Google Scholar
  15. 15.
    SFPark project, (accessed May 2013)
  16. 16.
    SiPark SSD car park guidance system, Siemens AG (2011) Google Scholar
  17. 17.
    IdentiPark, Nortech Internacional (2013)Google Scholar
  18. 18.
    Kang, S.B., Weiss, R.: Can We Calibrate a Camera Using an Image of a Flat, Textureless Lambertian Surface? In: Vernon, D. (ed.) ECCV 2000, Part II. LNCS, vol. 1843, pp. 640–653. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  19. 19.
    Torres, J., Menendez, J.M.: A practical algorithm to correct geometrical distortion of image acquisition cameras. In: IEEE International Conference on Image Processing, vol. 4, pp. 2451–2454 (2004)Google Scholar
  20. 20.
    Brown, D.C.: Decentering distortion of lenses. In: Photogrommetric Eng. Remore Sensing, pp. 444–462 (1966)Google Scholar
  21. 21.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  22. 22.
    Faugeras, O.D., Luong, Q.-T., Maybank, S.J.: Camera Self-Calibration: Theory and Experiments. In: 2nd European Conference on Computer Vision, pp. 321–334. Springer, London (1992)Google Scholar
  23. 23.
    Hartley, R., Zisserman, A.: Multiple View Geometry in computer vision. Cambridge University Press, Cambridge (2003)Google Scholar
  24. 24.
    Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: 6th ACM International Conference on Image and Video Retrieval, pp. 401–408. ACM, New York (2007)Google Scholar
  25. 25.
    Förstner, W., Moonen, B.: A metric for covariance matrices. Technical Report, Department of Geodesy and Geoinformatics, Stuttgart University (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Grupo de Aplicación de Telecomunicaciones VisualesUniversidad Politécnica de MadridMadridSpain

Personalised recommendations