Abstract: The technology disclosed teaches systems and methods for self-service installation of monitoring operations in an area of real space, the method including scanning the area of real space to generate a 3D representation of the area of real space, placing a camera at an initial location and orientation for monitoring a zone within the area of real space, configuring a computing device to be connected to a cloud network hosting an image processing service and couplable to the camera, coupling the camera to the computing device via a local connection using a unique identifier associated with the camera, and finetuning the camera placement to a calibrated location and orientation, wherein the finetuning is assisted by information received from a cloud-based application associated with the image processing service.

Abstract: The technology disclosed relates to detecting events and identifying items in detected events in an area of real space in a shopping store including a cashier-less checkout system. The system comprises an image sensor assembly comprising at least one narrow field of view (NFOV) image sensor and at least one wide field of view (WFOV) image sensor. The at least one NFOV image sensor produces raw image data of first-resolution frames of a corresponding field of view and the at least one WFOV image sensor produces raw image data of second-resolution frames of a corresponding field of view. The system comprises logic to provide at least a portion of a sequence of second-resolution frames produced by the WFOV image sensor to an event detection device configured to detect (i) a particular event and (ii) a location of the particular event in the area of real space.

Abstract: An apparatus for performing image analysis to identify human actions represented in an image, comprising: a joint-determination module configured to analyse an image depicting one or more people using a first computational neural network to determine a set of joint candidates for the one or more people depicted in the image; a pose estimation module configured to derive pose estimates from the set of joint candidates that estimate a body configuration for the one or more people depicted in the image; and an action-identification module configured to analyse a region of interest within the image identified from the derived pose estimates using a second computational neural network to identify an action performed by a person depicted in the image.

Abstract: An apparatus for performing image analysis to identify human actions represented in an image, comprising: a joint-determination module configured to analyse an image depicting one or more people using a first computational neural network to determine a set of joint candidates for the one or more people depicted in the image; a pose-estimation module configured to derive pose estimates from the set of joint candidates that estimate a body configuration for the one or more people depicted in the image; and an action-identification module configured to analyse a region of interest within the image identified from the derived pose estimates using a second computational neural network to identify an action performed by a person depicted in the image.

Abstract: A system for training a computational neural network to recognise objects and/or actions from images, the system comprising: a training unit, comprising: an input interface configured to receive: a plurality of images captured from one or more cameras, each image having an associated timestamp indicating the time the image was captured; a data stream containing a plurality of timestamps each associated with an object and/or action; the data stream being generated by a system in an operative field of view of the one or more cameras; an image identification unit configured to identify from the plurality of images a set of images that each have a timestamp that correlates to a timestamp associated with an object and/or action from the data stream; a data-labelling unit configured to determine, for each image of the set of images, an image label that indicates the probability the image depicts: (i) an object of each of a set of one or more specified object classes; and/or (ii) a specified human action in dependen

Abstract: An apparatus for performing image analysis to identify human actions represented in an image, comprising: a joint-determination module configured to analyse an image depicting one or more people using a first computational neural network to determine a set of joint candidates for the one or more people depicted in the image; a pose-estimation module configured to derive pose estimates from the set of joint candidates that estimate a body configuration for the one or more people depicted in the image; and an action-identification module configured to analyse a region of interest within the image identified from the derived pose estimates using a second computational neural network to identify an action performed by a person depicted in the image.

Abstract: An apparatus for performing image analysis to identify human actions represented in an image, comprising: a joint-determination module configured to analyse an image depicting one or more people using a first computational neural network to determine a set of joint candidates for the one or more people depicted in the image; a pose- estimation module configured to derive pose estimates from the set of joint candidates that estimate a body configuration for the one or more people depicted in the image; and an action-identification module configured to analyse a region of interest within the image identified from the derived pose estimates using a second computational neural network to identify an action performed by a person depicted in the image.

Abstract: A system for training a computational neural network to recognise objects and/or actions from images, the system comprising: a training unit, comprising: an input interface configured to receive: a plurality of images captured from one or more cameras, each image having an associated timestamp indicating the time the image was captured; a data stream containing a plurality of timestamps each associated with an object and/or action; the data stream being generated by a system in an operative field of view of the one or more cameras; an image identification unit configured to identify from the plurality of images a set of images that each have a timestamp that correlates to a timestamp associated with an object and/or action from the data stream; a data-labelling unit configured to determine, for each image of the set of images, an image label that indicates the probability the image depicts: (i) an object of each of a set of one or more specified object classes; and/or (ii) a specified human action in dependen