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Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes

EasyChair Preprint no. 6966

12 pagesDate: November 1, 2021


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 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.

Keyphrases: Multi-object tracking, Object re-identification, tracking-by-detection

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Oumayma Messoussi and Felipe Gohring de Magalhães and Francois Lamarre and Francis Perreault and Ibrahima Sogoba and Guillaume-Alexandre Bilodeau and Gabriela Nicolescu},
  title = {Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes},
  howpublished = {EasyChair Preprint no. 6966},

  year = {EasyChair, 2021}}
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