Unlu, Eren and Zenou, Emmanuel
and Rivière, Nicolas
Ordered Minimum Distance Bag-of-Words Approach for Aerial Object Identification.
(2017)
In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 29 August 2017 - 1 September 2017 (Lecce, Italy).
|
(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 1MB |
Abstract
Detecting potential aerial threats like drones with computer vision is at the paramount of interest for the protection of critical locations.This type of a system should prevent efficiently the false alarms caused by non-malign objects such as birds, which intrude the image plane. In this paper, we propose an improved version of a previously presented Speeded-up Robust Feature Transform (SURF) based algorithm, referred as Ordered Minimum Distance Bag-of-Words (omidBoW) to discriminate drones, birds and background from the patches, using an extended histogram set. We show that a SURF based object recognition can be well integrated to this context and this improved algorithm can increase accuracy up to 16% compared to regular bag-ofwords approach.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Additional Information: | ISBN 978-1-5386-2939-0/17 |
Audience (conference): | International conference proceedings |
Uncontrolled Keywords: | |
Institution: | Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE) French research institutions > Office National d'Etudes et Recherches Aérospatiales - ONERA (FRANCE) |
Laboratory name: | |
Statistics: | download |
Deposited On: | 25 Sep 2017 12:03 |
Repository Staff Only: item control page