Improving Chairlift Security with Deep Learning

Kevin Bascol (1, 2) , Rémi Emonet (1) , Elisa Fromont (1) , Raluca Debusschere (2)

1 LHC - Laboratoire Hubert Curien
2 Bluecime

This paper shows how state-of-the-art deep learning methods can be combined to successfully tackle a new classification task related to chairlift security using visual information. In particular, we show that with an effective architecture and some domain adaptation components, we can learn an end-to-end model that could be deployed in ski resorts to improve the security of chairlift passengers. Our experiments show that our method gives better results than already deployed hand-tuned systems when using all the available data and very promising results on new unseen chairlifts.

Mots clés: Domain adaptation; Image Classification; Convolutional neural networks; Deep learning

Domaines: Informatique [cs]; Apprentissage [cs.LG]

Kevin Bascol, Rémi Emonet, Elisa Fromont, Raluca Debusschere. Improving Chairlift Security with Deep Learning. International Symposium on Intelligent Data Analysis (IDA 2017), Oct 2017, Londres, United Kingdom. ⟨hal-01581392⟩