Abstract: A computer-implemented method for multimodal egocentric future prediction in a driving environment of an autonomous vehicle (AV) or an advanced driver assistance system (ADAS) equipped with a camera and comprising a trained reachability prior deep neural network (RPN), a trained reachability transfer deep neural network (RTN) and a trained future localization deep neural network (FLN) and/or a trained future emergence prediction deep neural network (EPN).

Abstract: A computer-implemented method for predicting multiple future trajectories of moving objects of interest in the environment of a monitoring device comprising a memory augmented neural network (MANN) comprising at least one trained encoder deep neural network, one trained decoder deep neural network and a key-value database storing keys corresponding to past trajectory encodings and associated values corresponding to associated future trajectory encodings.

Abstract: Some embodiments are directed to a processing method of a three-dimensional point cloud, including: obtaining a 3D point cloud from a predetermined view point of a depth sensor; extracting 3D coordinates and intensity data from each point of the 3D point cloud with respect to the view point; transforming 3D coordinates and intensity data into at least three two-dimensional spaces, namely an intensity 2D space function of the intensity data of each point, a height 2D space function of an elevation data of each point, and a distance 2D space function of a distance data between each point of 3D point cloud and the view point, defining a single multi-channel 2D space.

Abstract: Some embodiments are directed to a processing method of a three-dimensional point cloud, including: obtaining a 3D point cloud from a predetermined view point of a depth sensor; extracting 3D coordinates and intensity data from each point of the 3D point cloud with respect to the view point; transforming 3D coordinates and intensity data into at least three two-dimensional spaces, namely an intensity 2D space function of the intensity data of each point, a height 2D space function of an elevation data of each point, and a distance 2D space function of a distance data between each point of 3D point cloud and the view point, defining a single multi-channel 2D space.

Abstract: A method for detecting contour points of an object in an image obtained by a video camera comprising the steps of: (i) selecting a scan line of the image; (ii) identifying minimum intensity differences called transitions between pixels of the selected scan line; (iii) identifying plateaus at both ends of the identified transitions; (iv) determining contour points of the object between the identified plateaus; (v) generating a descriptor of the contour in one dimension; and (vi) beginning again with step (i) by selecting another scan line of the image according to a predefined order.

Abstract: A method for detecting contour points of an object in an image obtained by a video camera comprising the steps of: (i) selecting a scan line of the image; (ii) identifying minimum intensity differences called transitions between pixels of the selected scan line; (iii) identifying plateaus at both ends of the identified transitions; (iv) determining contour points of the object between the identified plateaus; (v) generating a descriptor of the contour in one dimension; and (vi) beginning again with step (i) by selecting another scan line of the image according to a predefined order.