• DocumentCode
    3388087
  • Title

    Vehicle Make and Model Recognition in CCTV footage

  • Author

    Saravi, Sara ; Edirisinghe, Eran A.

  • Author_Institution
    Dept. of Comput. Sci., Loughborough Univ., Loughborough, UK
  • fYear
    2013
  • fDate
    1-3 July 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    This paper presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD (coherent Point Drift) is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR (Vehicle Make and Model Recognition) task and may capture vehicles at different approaching angles. Also a novel ROI (Region Of Interest) segmentation is proposed. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximize the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates accuracy over 95% when tested in real CCTV footage with no prior camera calibration.
  • Keywords
    closed circuit television; image recognition; image segmentation; object detection; road vehicles; traffic engineering computing; video signal processing; CCTV video footage; CPD; LESH; ROI; VMMR; coherent point drift; local energy shape histogram; region-of-interest segmentation; vehicle make-and-model recognition; Educational institutions; Image recognition; Image resolution; Image segmentation; CCTV Footage; CPD; Coherent Point Drift; LESH; Local Energy Shape Histogram; Make and Model Recognition; VMMR;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Digital Signal Processing (DSP), 2013 18th International Conference on
  • Conference_Location
    Fira
  • ISSN
    1546-1874
  • Type

    conf

  • DOI
    10.1109/ICDSP.2013.6622720
  • Filename
    6622720