Abstract: A system for obtaining a prediction an action (at) of a vehicle (V), including a camera for acquiring a sequence of images (Ft) a convolutional neural network visual encoder which obtains a corresponding visual features vector (vt), one or more sensor that obtains a position of the vehicle (st) at the same time step (st), a Recurrent Neural Network configured to receive the visual features vector (vt) and position of the vehicle (st) at the time step (t) and to generate a prediction of the action (at) of the vehicle (V). The system comprising a command conditioned switch configured upon reception of a control command (ci) to select a corresponding branch of the Recurrent Neural Network. The system is configured to operate the selected corresponding branch to process the visual features vector (vt) and position of the vehicle (st) at the time step (t) to obtain the prediction of the action (at) of the vehicle (V).

Abstract: Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state-of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. The present approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation.

Abstract: Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state-of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. The present approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation.