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Objects Detection and Tracking in Highly Congested Traffic Using Compressed Video Sequences

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Computer Vision and Graphics (ICCVG 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7594))

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Abstract

The paper presents a model to detect and track vehicles in highly congested traffic using low quality (usually compressed) video sequences. Robustness of the model is provided by applying a data fusion for various detection and tracking algorithms. The surveys to find reliable detection algorithms were performed. Basing on the experiments, the model calibration and results were presented. The proposed model provides data, which can be used by traffic engineers in various microscopic traffic simulations.

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Author information

Authors and Affiliations

  1. Faculty of Transport, Silesian University of Technology, Krasińskiego 8, 40-019, Katowice, Poland

    Marcin Bernaś

Authors
  1. Marcin Bernaś
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Editor information

Editors and Affiliations

  1. Polish-Japanese Institute of Information Technology, 86 Koszykowa, 02-008, Warsaw, Poland

    Leonard Bolc

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Faculty of Applied Informatics and Mathematics, Department of Informatics, Warsaw University of Life Sciences (SGGW), Nowoursynowska 159, 02-776, Warsaw, Poland

    Leszek J. Chmielewski

  4. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warszawa, Poland

    Konrad Wojciechowski

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© 2012 Springer-Verlag Berlin Heidelberg

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Bernaś, M. (2012). Objects Detection and Tracking in Highly Congested Traffic Using Compressed Video Sequences. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_36

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  • DOI: https://doi.org/10.1007/978-3-642-33564-8_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33563-1

  • Online ISBN: 978-3-642-33564-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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