Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

Image data of a vehicle occupant are collected from a plurality of cameras. A dimensional model of substantially an entire body of the vehicle occupant is generated based on the image data. A gesture performed by the vehicle occupant is recognized based on the dimensional model. A vehicle subsystem is adjusted based on the gesture.

The invention relates to automatic discovery and evaluation of a motion by treating motion recognition as an optimization problem by considering a series of basic motions obtained by segmenting a subject motion. The method comprises segmenting time series data defining a motion of a subject into a plurality of segments, classifying each segment into a class for a basic motion by using time series data of the segment, and converting the motion of the subject to a sequence of high rank symbols in which each high rank symbol is formed from a series of the basic motions, wherein a function that calculates a score based on a set of a high rank symbol and a sequence of basic motions is provided and the motion of the subject is converted to the sequence of the high rank symbols by an optimization calculation using dynamic programming.

Described are techniques for uncertainty-aware anatomical landmark detection, using, for example, a deep reinforcement learning (DRL) anatomical landmark detection agent. For instance, a process can include generating one or more image features for an input medical image using a first sub-network of the anatomical landmark detection agent. A softmax layer of a second sub-network of the anatomical landmark detection agent can generate a plurality of discrete Q-value distributions for a set of allowable actions associated with movement of the agent within the medical image. An anatomical landmark location within the medical image can be predicted using the discrete Q-value distributions. An uncertainty can be determined for the predicted anatomical landmark location, based on an average full width half maximum (FWHM) calculated for the plurality of discrete Q-value distributions.

A method for reducing parking violations includes: receiving, by a controller of a vehicle, sign information of one or more traffic signs in an area surrounding the vehicle, wherein the sign information is a time series of data acquired by a set of cameras as the vehicle moves, using the vehicle as a frame of reference; identifying, by the controller, a potential parking spot in an area surrounding the vehicle; determining, by the controller, using the sign information, whether the potential parking spot is valid or invalid for the vehicle; and generating, by the controller, a notification if the potential parking spot is invalid.

A method for establishing sensorimotor programs includes specifying a concept relationship that relates a first concept to a second concept and establishes the second concept as higher-order than the first concept; training a first sensorimotor program to accomplish the first concept using a set of primitive actions; and training a second sensorimotor program to accomplish the second concept using the first sensorimotor program and the set of primitive actions.

A system for dispatching vehicles in a public transportation network may include a passenger monitoring system configured to monitor a number of passengers waiting at a stop in the transportation network, a vehicle dispatching system and a processing device. The processing device may apply a Markov Decision Process (MDP) model to determine a score for each of multiple decision rules, in which each score represents a number of passengers waiting at the stop at the end of a time interval, and use the scores to identify a number of waiting passengers at which a reserve vehicle should be dispatched. The system may use information received from the passenger monitoring system to determine a state at an instant of time, and determine whether a reserve vehicle should be dispatched based on the MDP model and cause the vehicle dispatch system to dispatch a reserve vehicle or retain a nominal vehicle.

Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

An object of the disclosure is to provide flexible and highly accurate activity detection means. Provided is an activity detection device including: an input unit that inputs an image sequence including a first image and a second image; an object detection unit that detects a first object in the image sequence; a component model unit that generates first characteristic information characterizing the first object and includes at least one individually trainable component model; and an activity detection unit that generates a first object state corresponding to the first object in the first image and a second object state corresponding to the first object in the second image based on the first characteristic information and determines an activity related to the first object based on the first and second object states.

A method for reducing parking violations includes: receiving, by a controller of a vehicle, sign information of one or more traffic signs in an area surrounding the vehicle, wherein the sign information is a time series of data acquired by a set of cameras as the vehicle moves, using the vehicle as a frame of reference; identifying, by the controller, a potential parking spot in an area surrounding the vehicle; determining, by the controller, using the sign information, whether the potential parking spot is valid or invalid for the vehicle; and generating, by the controller, a notification if the potential parking spot is invalid.