Real-Time Detection of Parked Vehicles from Multiple Image Streams

  • Kok-Leong Ong
  • Vincent C. S. Lee
Part of the Communications in Computer and Information Science book series (CCIS, volume 136)


We present a system to detect parked vehicles in a typical commercial parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.


Reference Image Detection Accuracy Markov Random Field Parking Space Parking System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chinrungrueng, J., Sunantachaikul, U., Triamlumlerd, S.: Smart Parking: An Application of Optical Wireless Sensor Network. In: IEEE/IPSJ International Symposium on Internet Workshops and Applications, p. 66 (2007)Google Scholar
  2. 2.
    Farhan, B., Murray, A.T.: Siting park-and-ride facilities using a multi-objective spatial optimization model. Computers and Operations Research 35(2), 445–456 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Foresti, G.L., Micheloni, C., Snidaro, L.: Event Classification for Automatic Visual-based Surveillance of Parking Lots. In: International Conference on Pattern Recognition, vol. 3, pp. 314–317 (2004)Google Scholar
  4. 4.
    Hodel-Widmer, T., Cong, S.: PSOS: Parking Space Optimization Service. In: 4th Swiss Transport Research Conference, Verit/Ascona, pp. 1–22 (March 2004)Google Scholar
  5. 5.
    Inaba, K., Shibui, M., Naganawa, T., Ogiwara, M., Yoshikai, N.: Intelligent Parking Reservation Service on the Internet. In: Symposium on Applications and the Internet-Workshops, San Diego, CA, USA, pp. 159–164 (2001)Google Scholar
  6. 6.
    Jones, W.: Parking 2.0: Meters Go High-Tech. IEEE Spectrum, 20 (2006)Google Scholar
  7. 7.
    Lee, C.H., Wen, M.G., Han, C.C., Kou, D.C.: An Automatic Monitoring Approach for Unsupervised Parking Lots in Outdoors. In: International Carnahan Conference on Security Technology, pp. 271–274 (October 2005)Google Scholar
  8. 8.
    Li, Y., Ma, R., Wang, L.: Intelligent Parking Negotiation Based on Agent Technology. In: WASE International Conference on Information Engineering, vol. 2, pp. 265–268 (2009)Google Scholar
  9. 9.
    Liu, Q., Lu, H., Zou, B., Li, Q.: Design and Development of Parking Guidance Information System based on Web and GIS Technology. In: 6th International Conference on ITS Telecommunications, Chengdu, China, pp. 1263–1266 (2006)Google Scholar
  10. 10.
    Masaki, I.: Machine-Vision Systems for Intelligent Transportation Systems. IEEE Intelligent Systems and their Applications, 13(6), 24–31 (1998)CrossRefGoogle Scholar
  11. 11.
    Mathijssen, A., Pretorius, A.: Verified Design of an Automated Parking Garage. In: Brim, L., Haverkort, B.R., Leucker, M., van de Pol, J. (eds.) FMICS 2006 and PDMC 2006. LNCS, vol. 4346, pp. 165–180. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Mo, Y., Su, Y.: Design of Parking Guidance and Information System in Shenzhen City. In: ISECS International Colloquium on Computing, Communication, Control, and Management, vol. 4, pp. 37–40 (August 2009)Google Scholar
  13. 13.
    Mohan, A., Papageorgiou, C., Poggio, T.: Example-Based Object Detection in Images by Components. IEEE Trans. Pattern Analysis and Machine Intelligence 23(4), 349–361 (2001)CrossRefGoogle Scholar
  14. 14.
    Mouskos, K.C., Maria Boile, N.A.P.: Technical Solutions to Overcrowded Park and Ride Facilities. Technical Report: FHWA-NJ-2007-011, University Transport Research Centre, Region 2 (2007),
  15. 15.
    Osuna, E., Freund, R., Girosi, F.: Support Vector Machines: Training and Applications. Tech. Rep. Massachusetts Institute of Technology, Cambridge, MA, USA (1997)Google Scholar
  16. 16.
    Rodier, C.J., Shaheen, S.A., Kemmerer, C.: Smart Parking Management Field Test: A Bay Area Rapid Transit (BART) District Parking Demonstration. Research Report: UCD-ITS-RR-08-32, Institute of Transportation Studies, University of California, Davis (2008),
  17. 17.
    Schneiderman, H., Kanade, T.: A Statistical Method for 3D Object Detection Applied to Faces and Cars. In: International Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, USA, pp. 1746–1759 (2000)Google Scholar
  18. 18.
    Vidal-Naquet, M., Ullman, S.: Object Recognition with Informative Features and Linear Classification. In: IEEE International Conference on Computer Vision, Nice, France, pp. 281–288 (2003)Google Scholar
  19. 19.
    Wu, Q., Huang, C., Wang, S., Chiu, W., Chen, T.: Robust Parking Space Detection Considering Inter-Space Correlation. In: IEEE International Conference on Multimedia and Expo., pp. 659–662 (July 2007)Google Scholar
  20. 20.
    Zhao, T., Nevatia, R.: Car Detection in Low Resolution Aerial Images. Image and Vision Computing, 710–717 (2001)Google Scholar
  21. 21.
    Zhong, H., Xu, J., Tu, Y., Hu, Y., Sun, J.: The Research of Parking Guidance and Information System based on Dedicated Short Range Communication. In: Proceedings of the IEEE Intelligent Transportation Systems, vol. 2, pp. 1183–1186 (October 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kok-Leong Ong
    • 1
  • Vincent C. S. Lee
    • 2
  1. 1.School of Information TechnologyDeakin UniversityAustralia
  2. 2.Faculty of Information TechnologyMonash UniversityAustralia

Personalised recommendations