A Smart Camera for Traffic Surveillance

  • Remigiusz Baran
  • Tomasz Ruść
  • Mariusz Rychlik
Part of the Communications in Computer and Information Science book series (CCIS, volume 429)

Abstract

An intelligent surveillance system based on visual information gathered by smart cameras, aimed at traffic monitoring with emphasis on traffic events caused by cars, is presented in the paper. The system components and their capabilities for automatic detection and recognition of selected parameters of cars, as well as different aspects of system efficiency, are described and discussed in detail. Smart facilities for Make and Model Recognition (MMR), License Plate Recognition (LPR) and Color Recognition (CR), embedded in the system in the form of their individual software implementations, are analyzed and their recognition rates detailed. Finally, a discussion of the system’s efficiency as a whole, with an insight into possible future improvements, is included in the conclusion.

Keywords

intelligent camera surveillance system vehicle recognition 

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References

  1. 1.
    Shi, Y., Lichman, S.: Smart Cameras: A Review. CCTV Focus (36), 34–43 & (37), 38–45 (2006)Google Scholar
  2. 2.
    IC Insights’ 2013 O-S-D Report (2013), http://www.icinsights.com/services/osd-report/ (viewed February 10, 2014)
  3. 3.
    Status of the CMOS Image Sensors Industry, http://www.prnewswire.com/news-releases/status-of-the-cmos-image-sensors-industry-187871741.html (viewed February 10, 2014)
  4. 4.
  5. 5.
  6. 6.
    Stahlschmidt, C., Gavriilidis, A., Velten, J., Kummert, A.: People Detection and Tracking from a Top-View Position Using a Time-of-Flight Camera. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2013. CCIS, vol. 368, pp. 213–223. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    http://www.salsa-autonomik.de/ (viewed February 10, 2014)
  8. 8.
    Janowski, L., Kozłowski, P., Baran, R., Romaniak, P., Glowacz, A., Rusc, T.: Quality assessment for a visual and automatic license plate recognition. Multimedia Tools and Applications 68(1), 23–40 (2014)CrossRefGoogle Scholar
  9. 9.
    Baran, R., Glowacz, A., Matiolanski, A.: The efficient real-and non-real-time make and model recognition of cars. Multimedia Tools and Applications (2013), doi:10.1007/s11042-013-1545-2.Google Scholar
  10. 10.
    Psyllos, A., Anagnostopoulos, C.N., Kayafas, E.: Vehicle model recognition from frontal view image measurements. Computer Standards & Interfaces 33(2), 142–151 (2011)CrossRefGoogle Scholar
  11. 11.
    http://www.springsource.org (viewed February 10, 2014)
  12. 12.
    http://exif.org/specifications.html (viewed February 10, 2014)
  13. 13.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up Robust Features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)CrossRefGoogle Scholar
  14. 14.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)MATHGoogle Scholar
  15. 15.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar
  16. 16.
    http://tesseract-ocr.repairfaq.org/ (viewed February 10, 2014)
  17. 17.
    ISO/IEC JTC1/SC29/WG11N6828, MPEG-7 Overview v10. MPEG, Palma de Mallorca (October 2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Remigiusz Baran
    • 1
  • Tomasz Ruść
    • 2
  • Mariusz Rychlik
    • 3
  1. 1.Faculty of Electrical Engineering, Automatics and Computer ScienceKielce University of TechnologyKielcePoland
  2. 2.Institute of PhysicsJan Kochanowski UniversityKielcePoland
  3. 3.University of Computer Engineering and TelecommunicationsKielcePoland

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