A consensus-based decentralized multi-robot approach is presented for reconstructing a discrete distribution of features, modeled as an occupancy grid map, that represent information contained in a bounded planar 2D environment, such as visual cues used for navigation or semantic labels associated with object detection. The robots explore the environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and estimate the feature distribution from their own measurements and the estimates communicated by neighboring robots, using a distributed Chernoff fusion protocol. Under this decentralized fusion protocol, each robot's feature distribution converges to the ground truth distribution in an almost sure sense.

Systems and methods are disclosed to objectively identify sub-second behavioral modules in the three-dimensional (3D) video data that represents the motion of a subject. Defining behavioral modules based upon structure in the 3D video data itself rather than using a priori definitions for what should constitute a measurable unit of action identifies a previously-unexplored sub-second regularity that defines a timescale upon which behavior is organized, yields important information about the components and structure of behavior, offers insight into the nature of behavioral change in the subject, and enables objective discovery of subtle alterations in patterned action. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

A method of visualising a meeting between one or more participants on a display includes, in an electronic processing device, the steps of: determining a plurality of signals, each of the plurality of signals being at least partially indicative of the meeting; generating a plurality of features using the plurality of signals, the features being at least partially indicative of the signals; generating at least one of: at least one phase indicator associated with the plurality of features, the at least one phase indicator being indicative of a temporal segmentation of at least part of the meeting; and at least one event indicator associated with the plurality of features, the at least one event indicator being indicative of an event during the meeting. The method also includes the step of causing a representation indicative of the at least one phase indicator and/or the at least one event indicator to be displayed on the display to thereby provide visualisation of the meeting.

The disclosure relates to systems and methods for determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.

Automated analog gauge reading is provided. The method comprises a computer system receiving input of an image and detecting at least one analog gauge in the image. The computer system corrects the orientation of the analog gauge in the image and detects scene text and tick labels on the analog gauge. The computer system determines a position of a pointer on the analog gauge relative to the scene text and outputs a gauge reading value based on an arithmetic progression of tick labels and angle of the pointer with respect to minimum and maximum values on the analog gauge.

The present invention relates to a method and system for accurately predicting future trajectories of observed objects in dense and ever-changing city environments. More particularly, the present invention relates to the use of prior trajectories extracted from mapping data to estimate the future movement of an observed object. As an example, an observed object may be a moving vehicle. Aspects and/or embodiments seek to provide a method and system for predicting future movements of a newly observed object, such as a vehicle, using motion prior data extracted from map data.

The present invention relates to a method and system for accurately predicting future trajectories of observed objects in dense and ever-changing city environments. More particularly, the present invention relates to substantially continuously tracking and estimating the future movements of an observed object. As an example, an observed object may be a moving vehicle, for example along a path or road. Aspects and/or embodiments seek to provide an end to end method and system for substantially continuously tracking and predicting future movements of a newly observed object, such as a vehicle, using motion prior data extracted from map data.

A method of recognizing movement includes measuring, by an inertial sensor, a first unit of inertia of an object. In addition, the method includes measuring a three dimensional shape of the object. Further, the method includes receiving, by a processor, a signal representative of the measured first unit of inertia from the inertial sensor and a signal representative of the measured shape from the depth sensor. Still further, the method includes determining a type of movement of the object based on the measured first unit of inertia and the measured shape utilizing a classification model.

A method of determining a composite action from a video clip, using a conditional random field (CRF), the method includes determining a plurality of features from the video clip, each of the features having a corresponding temporal segment from the video clip. The method may continue by determining, for each of the temporal segments corresponding to one of the features, an initial estimate of an action unit label from a corresponding unary potential function, the corresponding unary potential function having as ordered input the plurality of features from a current temporal segment and at least one other of the temporal segments. The method may further include determining the composite action by jointly optimizing the initial estimate of the action unit labels.

One or more aspects of utility decomposition with deep corrections are described herein. An entity may be detected within an environment through which an autonomous vehicle is travelling. The entity may be associated with a current velocity and a current position. The autonomous vehicle may be associated with a current position and a current velocity. Additionally, the autonomous vehicle may have a target position or desired destination. A Partially Observable Markov Decision Process (POMDP) model may be built based on the current velocities and current positions of different entities and the autonomous vehicle. Utility decomposition may be performed to break tasks or problems down into sub-tasks or sub-problems. A correction term may be generated using multi-fidelity modeling. A driving parameter may be implemented for a component of the autonomous vehicle based on the POMDP model and the correction term to operate the autonomous vehicle autonomously.