Sony Group Corporation

Sony Corporation

Sangxia Huang

Anders Isberg

Olivier Moliner

Andrej Petef

Mostafa Attariani

Karin Ahltin

Chengxi Li

David Strömbäck

An electronic device includes memory circuitry, and processor circuitry having an action detection circuitry configured to operate according to an action detection model for detecting an action based on a machine-learning scheme. The processor circuitry being configured to obtain sensor data; generate, based on the sensor data, a set of features associated with a frame; determine, based on the set, using the action detection model, whether the frame corresponds to a sub-action; apply a nondeterministic finite automaton, NFA, scheme, to the determined sub-action for the frame, wherein the NFA scheme has a set of states associated with corresponding sub-actions and is configured to output one or more action classes; determine, using the NFA scheme, an action class; detect the action based on the action class; and increment an action counter based on the detected action.

Electronic device and a related method for detecting and counting an action

An observer device () listens for advertising messages from broadcast devices () in an environment, transmits scan request messages to broadcast devices, and subsequently receives scan response messages. The observer device () can connect to a broadcast device () and configure the broadcast device () to a scanning mode. The broadcast device () selected for scanning listens for advertising messages from other broadcast devices (). The broadcast device () relays information related to these other broadcast devices () back to the observer device ().

Method and apparatus for searching and relaying discovery of devices

A method for processing images of animals obtains first training data, which includes first images of animals and first annotation data including first location data for predefined feature points on at least one animal in each image in the first images, and trains a first machine learning-based model (MLM) by use of the first training data to generate a first trained MLM. The method obtains second images of animals, operates the first trained MLM on the second images to generate second annotation data comprising second location data for the predefined feature points on at least one animal in each image in the second images, and aggregates at least a subset of the first and second images, and a corresponding subset of the first and second annotation data, into second training data, which thereby is expanded in relation to the first training data.

Training machine learning-based models for animal feature detection

A method for rescue support involves distributing transponder devices to individuals in a geographic region and causing the transponder devices to be activated. If a large-scale emergency situation occurs, a fleet of unmanned aerial vehicles are operated within the geographic region to receive distress signals transmitted by the activated transponder devices. A computer system processes the distress signals to obtain detection data for the individuals, the detection data comprising a location of the respective transponder device and a health status of the respective individual. The computer system generates, based on the location and health status of the respective individual, a prioritization chart for the geographic region, the prioritization chart being indicative of one or more sub-regions to be prioritized for a rescue operation.

Rescue support in large-scale emergency situations

Triangulation is applied, by a method and a device, to determine a scene point location in a global coordinate system, GCS, based on location coordinates for observed image points in plural views of a scene, and global-local transformation matrices for the views. The local coordinates are given in local coordinate systems, LCSs, arranged to define distance coordinate axes that are perpendicular to image planes of the views. The scene point location is determined by minimizing a plurality of differences between first and second estimates of the scene point location in the LCSs, the first estimates being given by linear scaling of the location coordinates, and the second estimate being given by operating the transformation matrices on the scene point location. An improved robustness to observation errors is achieved by defining the plurality of differences to penalize differences that includes the linear scaling as applied along the distance coordinate axes.

Multiple view triangulation with improved robustness to observation errors

A device determines the positioning of objects in a scene by implementing a robust and deterministic method. The device obtains object detection data (ODD) which identifies the objects and locations of reference points of the objects in views of the scene. The obtained ODD is processed to identify a first image object of a first view as a mirror reflection of a real object. A virtual view associated with a virtual camera position is created, including the ODD associated with the first image object of the first view. The ODD associated with the first image object is removed from the first view. Based on the ODD associated with at least said virtual view and a further view of the one or more views, a position of said first image object is computed.

Multi-view positioning using reflections

An electronic device includes a memory circuitry, an interface circuitry, a sensor circuitry, and a processor circuitry having a predictor circuitry configured to operate according to a prediction model. The processor circuitry is configured to obtain sensor data. The processor circuitry is configured to determine, based on the sensor data, using the predictor circuitry, a predicted time parameter indicative of a prediction of a time slot to initiate an establishment of a network with one or more other electronic devices. The processor circuitry is configured to initiate the establishment of the network with the one or more other electronic devices according to the predicted time parameter.

Electronic device and related methods for predicting initiation of establishment of a network with one or more other electronic devices

A device determines positions of objects in a scene. The device obtains object detection data (ODD) which identifies the objects and locations of reference points of the objects in 2D images of the scene. The device processes the ODD to generate candidate association data (CAD) which associates pairs of objects between the images, computes estimated 3D positions in the scene for associated pairs of objects in the CAD, and performs clustering of the estimated positions. The device further generates, based on estimated 3D positions in one or more clusters, final association data (FAD) which associates one or more objects between the images, and computes one or more final 3D positions in the scene for one or more reference points of the one or more objects in the FAD. The final 3D position(s) represent the 3D position or the 3D pose of the respective object in the scene.

Multi-view three-dimensional positioning

A method for extrinsic calibration of cameras in a monitoring system, performed by a calibration device, includes obtaining preliminary calibration data of the cameras, obtaining a preliminary pose for an object observed by the cameras, and determining calibration data that results in an optimization of an objective function using the preliminary calibration data and the preliminary pose as starting values for the optimization. The method further includes defining the objective function with calibration data and pose as unknown variables and including a first function yielding aggregated reprojection error, and a second function yielding pose quality based on relative orientation and relative distance between 3D points of the pose. The second function enables improved accuracy of the calibration data and may be configured to yield pose quality for individual poses or motion quality for a time sequence of poses.

Extrinsic calibration of multi-camera system

An electronic device is configured to analyze movement data generated by a sensor (e.g., an accelerometer assembly) in the electronic device to determine if the electronic device should be activated from a reduced power state. A control circuit in the electronic device can compare the movement data to a predetermined movement pattern to determine whether the movement should be ignored, or if the movement is consistent with a user shaking the electronic device to activate the electronic device. As a result of this approach, erroneous activation of the electronic device is avoided, thereby preventing the electronic device from needlessly depleting its battery before deployment by the end user.

Method and system for device activation

An object monitoring system includes a plurality of wireless sensor devices and at least one user device A method for controlling a response time of one of the wireless sensor devices in such a system includes determining one or more metrics representing a present state of an object; and calculating a probability that one of at least one user devices will interact with the wireless sensor device within a pre-determined time period by executing a trained model with the one or more determined metrics as input and controlling the response time of the wireless sensor device based on the calculated probability.

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Malmö, Sweden

Sangxia Huang