• Evaluation of CNN architectures for gait recognition based on optical flow maps 

      Castro, Francisco M.; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolas; López-Tapia, S.; Pérez de la Blanca, N. (2017)
      This work targets people identification in video based on the way they walk (\ie gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors ...
    • Gait recognition and fall detection with inertial sensors 

      Delgado-Escaño, Rubén; Castro, Francisco M.; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolas (2019-11-26)
      In contrast to visual information that is recorded by cameras placed somewhere, inertial information can be obtained from mobile phones that are commonly used in daily life. We present in this talk a general deep learning ...
    • Gait recognition applying Incremental learning 

      Castro, Francisco M.; Marín-Jiménez, Manuel J.; Guil-Mata, Nicolas; Schmid, Cordelia; Alahari, Karteek (2019-11-25)
      when new knowledge needs to be included in a classifier, the model is retrained from scratch using a huge training set that contains all available information of both old and new knowledge. However, in this talk, we present ...
    • A weakly-supervised approach for discovering common objects in airport video surveillance footage 

      Castro Payan, Francisco Manuel; Delgado-Escaño, Rubén; Guil-Mata, Nicolas; Marín-Jiménez, Manuel J. (2019-07-22)
      Object detection in video is a relevant task in computer vision. Standard and current detectors are typically trained in a strongly supervised way, what requires a huge amount of labelled data. In contrast, in this paper ...