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Proceedings Paper

Linear classification of chairlift images for presence analysis
Author(s): Julien Muzeau; Patricia Ladret; Pascal Bertolino
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Paper Abstract

In the recent past years, innumerable techniques more complex than the others have emerged in computer vision. They have been applied to many fields and, thanks to the tremendous computational power one has access to nowadays, have made possible more and more elaborate applications. In this article, we propose a classification tool, using hand-crafted interpretable (statistical and digital imaging) features, in order to confirm or invalidate the presence of passengers on skilift vehicles in moutain ranges. More precisely, Linear Discriminant Analysis, which is a dimensionality reduction alongside classification technique, and its less restrictive variant Quadratic Discriminant Analysis, are applied. One of the paper’s objectives consists in illustrating the famous law of parsimony, also known as Occam’s razor, in the sense that the simpler solutions should be considered first and more complex models should be built afterwards if needed.

Paper Details

Date Published: 16 July 2019
PDF: 8 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117205 (16 July 2019); doi: 10.1117/12.2521686
Show Author Affiliations
Julien Muzeau, Univ. Grenoble Alpes, Grenoble INP, CNRS (France)
Patricia Ladret, Univ. Grenoble Alpes, Grenoble INP, CNRS (France)
Pascal Bertolino, Univ. Grenoble Alpes, Grenoble INP, CNRS (France)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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