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Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes

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Advances in Visual Computing (ISVC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13018))

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Abstract

Detecting and tracking vehicles in urban scenes is a crucial step in many traffic-related applications as it helps to improve road user safety among other benefits. Various challenges remain unresolved in multi-object tracking (MOT) including target information description, long-term occlusions and fast motion. We propose a multi-vehicle detection and tracking system following the tracking-by-detection paradigm that tackles the previously mentioned challenges. Our MOT method extends an Intersection-over-Union (IOU)-based tracker with vehicle re-identification features. This allows us to utilize appearance information to better match objects after long occlusion phases and/or when object location is significantly shifted due to fast motion. We outperform our baseline MOT method on the UA-DETRAC benchmark while maintaining a total processing speed suitable for online use cases.

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Author information

Authors and Affiliations

  1. Polytechnique Montreal, Montreal, QC, H3T 1J4, Canada

    Oumayma Messoussi, Felipe Gohring de Magalhães, Guillaume-Alexandre Bilodeau & Gabriela Nicolescu

  2. Cysca Technologies, Montreal, QC, H2Y 1N9, Canada

    Francois Lamarre, Francis Perreault & Ibrahima Sogoba

Authors
  1. Oumayma Messoussi
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  2. Felipe Gohring de Magalhães
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  3. Francois Lamarre
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  4. Francis Perreault
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  5. Ibrahima Sogoba
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  6. Guillaume-Alexandre Bilodeau
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  7. Gabriela Nicolescu
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Corresponding author

Correspondence to Oumayma Messoussi .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Texas at Arlington, Arlington, TX, USA

    Vassilis Athitsos

  3. University of South Carolina, Columbia, SC, USA

    Tong Yan

  4. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  5. School of Engineering and Computing, University of Durham, Durham, Durham, UK

    Frederick Li

  6. Airbnb, New York, NY, USA

    Conglei Shi

  7. Peking University, Beijing, China

    Xiaoru Yuan

  8. Purdue University, West Lafayette, IN, USA

    Christos Mousas

  9. IST, School of Modeling, Simulation, and Training, Orlando, FL, USA

    Gerd Bruder

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Messoussi, O. et al. (2021). Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13018. Springer, Cham. https://doi.org/10.1007/978-3-030-90436-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-90436-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90435-7

  • Online ISBN: 978-3-030-90436-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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