Linear classification of chairlift images for presence analysis

Julien Muzeau; Patricia Ladret; Pascal Bertolino

doi:10.1117/12.2521686

16 July 2019 Linear classification of chairlift images for presence analysis

Julien Muzeau, Patricia Ladret, Pascal Bertolino

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 1117205 (2019) https://doi.org/10.1117/12.2521686
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

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.

Citation Download Citation

Julien Muzeau, Patricia Ladret, and Pascal Bertolino "Linear classification of chairlift images for presence analysis", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117205 (16 July 2019); https://doi.org/10.1117/12.2521686

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