Kevin Bascol

Doctoral researcher at Laboratoire Hubert Curien and Bluecime



Laboratoire Hubert Curien,
18 Rue du Professeur Benoît Lauras
42000 Saint-Étienne, FRANCE


(+33) (0)4 77 91 57 50




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Deep Learning for anomaly detection in Chairlifts videos

My PhD is under the supervision of Elisa Fromont and Rémi Emonet at Laboratoire Hubert Curien in Saint-Etienne (France).

Thanks to a CIFRE contract my PhD is in collaboration with Bluecime, a company based in Grenoble (France).
Bluecime proposes a system with a camera placed at the first tower of a chairlift and, by image processing techniques, an alarm is triggered if the passengers are potentially at risks (for example if they didn't close the restraining bar).

My thesis consists in using machine learning methods (mostly deep-learning) to decide if the alarm should be triggered or not. Using machine learning could drastically reduce the time and resources required for the set-up of the current system.


Improving Chairlift Security with Deep Learning

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.

  title={Improving Chairlift Security with Deep Learning},
  author={Bascol, Kevin and Emonet, R{\'e}mi and Fromont, Elisa and Debusschere, Raluca},
  booktitle={International Symposium on Intelligent Data Analysis (IDA 2017)},
  year = {2017}
Kevin Bascol, Rémi Emonet, Elisa Fromont, Raluca Debusschere - IDA 2017 pdf

Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders

We study the use of feed-forward convolutional neural networks for the unsupervised problem of mining recurrent temporal patterns mixed in multivariate time series. Traditional convolutional autoencoders lack interpretability for two main reasons: the number of patterns corresponds to the manually-fixed number of convolution filters, and the patterns are often redundant and correlated. To recover clean patterns, we introduce different elements in the architecture, including an adaptive rectified linear unit function that improves patterns interpretability, and a group-lasso regularizer that helps automatically finding the relevant number of patterns. We illustrate the necessity of these elements on synthetic data and real data in the context of activity mining in videos.

  title={Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders},
  author={Bascol, Kevin and Emonet, R{\'e}mi and Fromont, Elisa and Odobez, Jean-Marc},
  booktitle={The joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2016)},
Kevin Bascol, Rémi Emonet, Elisa Fromont, Jean-Marc Odobez - SSPR 2016 pdf