A vehicle is provided that includes an imaging device located on the vehicle and oriented to capture images, memory storing one or more pet profiles, and a controller processing the captured images to detect a pet and determining at least one of an estimated pose of the pet and a gait of the pet, wherein the processor determines a bone structure of the pet from the at least one estimated pose and gait and compares the bone structure to the one or more pet profiles to detect an authorized pet, the controller controlling one or more vehicle related features based on the detected authorized pet.

An exercise assisting device and exercise assisting method are provided. The device performs the following operations: transmitting a first control signal, the first control signal is related to a motion demonstration video corresponding to an exercise course data; receiving a video stream of a user; analyzing the video stream to generate a motion recognition result corresponding to the exercise course data of the user, and determining whether a motion target value is achieved according to the motion recognition result and the exercise course data; and when the motion target value is not achieved, the motion recognition result and the motion target value are input into an expert suggestion model to generate a follow-up motion suggestion and to determine a follow-up motion demonstration video corresponding to the follow-up motion suggestion.

In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

An image processing apparatus comprising, a tracking unit configured to detect an object and track the object in images to be processed, the images being sequential with respect to time, a determining unit configured to determine a stay time for which the object stays, on the basis of a result of the tracking, and an associating unit configured to specify, on the basis of the result of the tracking, one predetermined location from one or more predetermined locations included in the images to be processed, and associate the specified one predetermined location with the stay time.

A system includes one or more sensors coupled to equipment; one or more 5G antennas; one or more 5G transceivers coupled to the one or more 5G antennas; and a processor to control one or more wireless or antenna network resources to deliver wireless communication to a remote device.

A control system and a method for predicting a location of dynamic objects, for example, of pedestrians, which are able to be detected by the sensors of a vehicle. The control system includes a multitude of sensors and a processing system, which is configured to combine with a first program the objects that are detected by the multitude of sensors to form an object list, each entry of the object list encompassing the location, a speed and an open route for each of the objects, and the object list including a time stamp; and to determine with a second program for at least a portion of the dynamic objects an additional object list from a predefined number of object lists, the additional object list including a time stamp for a future point in time and encompassing at least the location of the dynamic objects.

A system and method leverages understanding of complex dribbling video clips by representing a video sequence with a single Dribble Energy Image (DEI) that is informative for dribbling styles recognition. To overcome the shortage of labelled data, a dataset of soccer video clips employs Mask-RCNN to segment out dribbling players and OpenPose to obtain joints information of dribbling players. To solve issues caused by camera motions in highlight soccer videos, the system registers a video sequence to generate a single image representation DEI and dribbling styles classification.

In accordance with some embodiments, an apparatus that controls sensor paths for privacy protection is provided. The apparatus includes a housing arranged to hold a second device. The apparatus obtains first sensor data that includes a biometric marker associated with a user. The apparatus controls sensor paths by obtaining the first sensor data using sensors on the second device, on the apparatus, and/or on a supplemental functional device. The apparatus further generates second sensor data by masking the biometric marker associated with the user in the first sensor data. The apparatus additionally controls the sensor paths by providing the second sensor data from the first apparatus to the second device.

Example vehicle unlocking methods and apparatus are described. In one example method, wakeup keyword detection is performed on an obtained voice signal. If the voice signal includes a wakeup keyword, an acoustic source direction is determined, and a target camera is indicated to collect image data. The target camera is a target camera that is installed on the vehicle and whose angle of view can ensure that the acoustic source direction is captured. Then, the vehicle is unlocked based on the image data collected by the target camera.

The purpose of the present invention is to provide a fall risk evaluation system whereby risk of falling of an elderly person or other person to be managed can be easily evaluated on the basis of a captured image of daily life, instead of by a physical therapist, etc. To achieve this purpose, the present invention is a fall risk evaluation system comprising a stereo camera and a fall risk evaluation device, the fall risk evaluation device being provided with: a person authentication unit for authenticating a person to be managed who has been imaged by the stereo camera; a person tracking unit for tracking the person to be managed who is authenticated by the person authentication unit; an action extraction unit for extracting walking by the person to be managed; a feature value calculation unit for calculating a feature value of the walking extracted by the action extraction unit; an integration unit for generating integrated data obtained by integrating the outputs of the person authentication unit, the person tracking unit, the action extraction unit, and the feature value calculation unit; a fall index calculation unit for calculating a fall index value of the person to be managed, on the basis of a plurality of integrated data generated by the integration unit; and a fall risk evaluation unit for comparing the fall index value calculated by the fall index calculation unit and a threshold value to evaluate the risk of falling of the person to be managed.