Abstract: Systems and techniques are provided for calibrating cameras in a real space for tracking puts and takes of items by subjects. The method includes first processing one or more selected images captured by an extrinsic calibration tool to extract from the images, a three-dimensional (3D) point cloud of points corresponding to displays or structures that remain substantially immobile in the real space, and a second processing of a second set of images captured by a camera installation in the real space to match points in the second set of images to the point cloud. Differences in position of matching points can be used to determine transformation information therebetween, which can be used to calibrate the installed cameras.

Abstract: Techniques for managing coverage constraints are provided for determining an improved camera coverage plan including a number, a placement, and a pose of cameras that are arranged to track puts and takes of items by subjects in a three-dimensional real space. The method includes receiving an initial camera coverage plan including a three-dimensional map of a real space, an initial number and initial pose of a plurality of cameras and a camera model including characteristics of the cameras. The method can iteratively apply a machine learning process to an objective function of number and poses of cameras, and subject to a set of constraints, obtain an improved camera coverage plan. The improved camera coverage plan is provided to an installer to arrange cameras to track puts and takes of items by subjects in the three-dimensional real space.

Abstract: Automated techniques provide for recalibrating cameras configured for monitoring an area of real space. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. Transformation information between the selected images and the set of calibration images is obtained based on one or more features extracted from the selected images using a trained neural network classifier corresponding to points located at relatively immobile structures. The features extracted from the selected images match features in the set of calibration images. Camera calibrations can be updated when transform information between features matched meets or exceeds a threshold.

Abstract: Automated techniques provide for recalibrating cameras in a real space in which puts and takes of items are tracked. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. The first processing includes a process step to match one or more features from the selected images with features extracted from the set of calibration images using a trained neural network classifier. The features correspond to points located at displays or structures that remain substantially immobile. Camera calibrations can be updated when transform information between features matched meets or exceeds a threshold.

Abstract: Techniques for managing coverage constraints are provided for determining an improved camera coverage plan including a number, a placement, and a pose of cameras that are arranged to track puts and takes of items by subjects in a three-dimensional real space. The method includes receiving an initial camera coverage plan including a three-dimensional map of a real space, an initial number and initial pose of a plurality of cameras and a camera model including characteristics of the cameras. The method can iteratively apply a machine learning process to an objective function of number and poses of cameras, and subject to a set of constraints, obtain an improved camera coverage plan. The improved camera coverage plan is provided to an installer to arrange cameras to track puts and takes of items by subjects in the three-dimensional real space.

Abstract: Systems and techniques are provided for determining an improved camera coverage plan including a number, a placement, and a pose of cameras that are arranged to track puts and takes of items by subjects in a three-dimensional real space. The method includes receiving an initial camera coverage plan including a three-dimensional map of a real space, an initial number and initial pose of a plurality of cameras and a camera model including characteristics of the cameras. The method can iteratively apply a machine learning process to an objective function of number and poses of cameras, and subject to a set of constraints, obtain an improved camera coverage plan. The improved camera coverage plan is provided to an installer to arrange cameras to track puts and takes of items by subjects in the three-dimensional real space.

Abstract: Systems and techniques are provided for determining an improved camera coverage plan including a number, a placement, and a pose of cameras that are arranged to track puts and takes of items by subjects in a three-dimensional real space. The method includes receiving an initial camera coverage plan including a three-dimensional map of a real space, an initial number and initial pose of a plurality of cameras and a camera model including characteristics of the cameras. The method can iteratively apply a machine learning process to an objective function of number and poses of cameras, and subject to a set of constraints, obtain an improved camera coverage plan. The improved camera coverage plan is provided to an installer to arrange cameras to track puts and takes of items by subjects in the three-dimensional real space.

Abstract: Automated techniques provide for recalibrating cameras in a real space in which puts and takes of items are tracked. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. The first processing includes a process step to match one or more features from the selected images with features extracted from the set of calibration images using a trained neural network classifier. The features correspond to points located at displays or structures that remain substantially immobile. Camera calibrations can be updated when transform information between features matched meets or exceeds a threshold.

Abstract: Systems and techniques are provided for calibrating cameras in a real space for tracking puts and takes of items by subjects. The method includes first processing one or more selected images captured by an extrinsic calibration tool to extract from the images, a three-dimensional (3D) point cloud of points corresponding to displays or structures that remain substantially immobile in the real space, and a second processing of a second set of images captured by a camera installation in the real space to match points in the second set of images to the point cloud. Differences in position of matching points can be used to determine transformation information therebetween, which can be used to calibrate the installed cameras.

Abstract: Systems and techniques are provided for recalibrating cameras in a real space for tracking puts and takes of items by subjects. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. The first processing includes a process step to extract a plurality of feature descriptors from the images. The first processing also includes a process step to match one or more feature descriptors as extracted from the selected images with feature descriptors extracted from the set of calibration images that were used to calibrate the cameras previously. The feature descriptors correspond to points located at displays or structures that remain substantially immobile.

Abstract: Systems and techniques are provided for determining an improved camera coverage plan including a number, a placement, and a pose of cameras that are arranged to track puts and takes of items by subjects in a three-dimensional real space. The method includes receiving an initial camera coverage plan including a three-dimensional map of a real space, an initial number and initial pose of a plurality of cameras and a camera model including characteristics of the cameras. The method can iteratively apply a machine learning process to an objective function of number and poses of cameras, and subject to a set of constraints, obtain an improved camera coverage plan. The improved camera coverage plan is provided to an installer to arrange cameras to track puts and takes of items by subjects in the three-dimensional real space.

Abstract: Systems and techniques are provided for recalibrating cameras in a real space for tracking puts and takes of items by subjects. The method includes first processing one or more selected images selected from a plurality of sequences of images received from a plurality of cameras calibrated using a set of calibration images that were used to calibrate the cameras previously. The first processing includes a process step to extract a plurality of feature descriptors from the images. The first processing also includes a process step to match one or more feature descriptors as extracted from the selected images with feature descriptors extracted from the set of calibration images that were used to calibrate the cameras previously. The feature descriptors correspond to points located at displays or structures that remain substantially immobile.

Abstract: Systems and techniques are provided for determining an improved camera coverage plan including a number, a placement, and a pose of cameras that are arranged to track puts and takes of items by subjects in a three-dimensional real space. The method includes receiving an initial camera coverage plan including a three-dimensional map of a real space, an initial number and initial pose of a plurality of cameras and a camera model including characteristics of the cameras. The method can iteratively apply a machine learning process to an objective function of number and poses of cameras, and subject to a set of constraints, obtain an improved camera coverage plan. The improved camera coverage plan is provided to an installer to arrange cameras to track puts and takes of items by subjects in the three-dimensional real space.

Abstract: The technology disclosed allows for remote distributed computing and storage of vehicle data obtained from one or more vehicles. Specifically, the technology disclosed is capable of determining, at a vehicle, resource requirements for a software application. The technology disclosed is also capable of selecting a computation resource from a plurality of computation resources based on one or more selection policies for meeting the resource requirements, sending the vehicle data from the vehicle to the selected computation resource, receiving at the vehicle data from the selected computation resource and utilizing the data obtained from the computation resource and data in the vehicle in the software application.

Abstract: The technology disclosed allows for remote distributed computing and storage of data obtained from one or more client devices. Specifically, the technology disclosed is capable of receiving, at the client device, data from at least one data source. The technology discloses is also capable of determining, at the client device, data information associated with the data from the at least one data source, selecting a computation resource from a plurality of computation resources based on one or more selection policies, where the selection policies are associated with one or more of location constraints, time constraints, and data type constraints and sending the data from the client device to the selected computation resource for processing.

Abstract: Embodiments generally relate to distributed computing of vehicle data. In some embodiments, a method includes determining, at a vehicle, resource requirements for a software application. The method further includes selecting a computation resource from a plurality of computation resources based on one or more selection policies for meeting the resource requirements. The method further includes sending the vehicle data from the vehicle to the selected computation resource. The method further includes receiving at the vehicle data from the selected computation resource. The method further includes utilizing the data obtained from the computation resource and data in the vehicle in the software application.

Abstract: Embodiments generally relate to distributed computing of data. In some embodiments, a method includes receiving, at a client device, data from at least one data source. The method further includes determining, at the client device, data information associated with the data from the at least one data source. The method further includes selecting a computation resource from a plurality of computation resources based on one or more selection policies, where the selection policies are associated with one or more of location constraints, time constraints, and data type constraints. The method further includes sending the data from the client device to the selected computation resource for processing.