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Proceedings Paper

Vision-based industrial automatic vehicle classifier
Author(s): Timur Khanipov; Ivan Koptelov; Anton Grigoryev; Elena Kuznetsova; Dmitry Nikolaev
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Paper Abstract

The paper describes the automatic motor vehicle video stream based classification system. The system determines vehicle type at payment collection plazas on toll roads. Classification is performed in accordance with a preconfigured set of rules which determine type by number of wheel axles, vehicle length, height over the first axle and full height. These characteristics are calculated using various computer vision algorithms: contour detectors, correlational analysis, fast Hough transform, Viola-Jones detectors, connected components analysis, elliptic shapes detectors and others. Input data contains video streams and induction loop signals. Output signals are vehicle enter and exit events, vehicle type, motion direction, speed and the above mentioned features.

Paper Details

Date Published: 14 February 2015
PDF: 5 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944511 (14 February 2015); doi: 10.1117/12.2181557
Show Author Affiliations
Timur Khanipov, Institute for Information Transmission Problems (Russian Federation)
Ivan Koptelov, Visillect Service Ltd. (Russian Federation)
Anton Grigoryev, Moscow Institute of Physics and Technology (Russian Federation)
Elena Kuznetsova, Institute for Information Transmission Problems (Russian Federation)
Dmitry Nikolaev, Institute for Information Transmission Problems (Russian Federation)


Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)

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