Vehicle Detection and Tracking From Surveillance Cameras in Urban Scenes

• URL: http://arxiv.org/abs/2109.12414v1
• Date: Sat, 25 Sep 2021 18:21:44 GMT
• Title: Vehicle Detection and Tracking From Surveillance Cameras in Urban Scenes
• Authors: Oumayma Messoussi, Felipe Gohring de Magalhaes, Francois Lamarre, Francis Perreault, Ibrahima Sogoba, Guillaume-Alexandre Bilodeau, Gabriela Nicolescu
• Abstract summary: We propose a multi-vehicle detection and tracking system following the tracking-by-detection paradigm. Our method extends an Intersection-over-Union (IOU)-based tracker with vehicle re-identification features. We outperform our baseline MOT method on the UA-DETRAC benchmark while maintaining a total processing speed suitable for online use cases.
• Score: 9.54261903220931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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