Provided are a method and an apparatus for recognizing a gesture, an electronic device and a storage medium. In one or more embodiments, the method includes: detecting at least one hand region from a video image and obtaining hand image information of each of the at least one hand region; obtaining hand motion information of each of the at least one hand region by tracking the at least one hand region; determining a gesture corresponding to each of the at least one hand region according to the hand image information and/or the hand motion information of each of the at least one hand region; wherein the gesture comprises at least one of a single-hand static gesture, a single-hand dynamic gesture, a double-hand static gesture or a double-hand dynamic gesture.

A generative network may be learned in an adversarial setting with a goal of modifying synthetic data such that a discriminative network may not be able to reliably tell the difference between refined synthetic data and real data. The generative network and discriminative network may work together to learn how to produce more realistic synthetic data with reduced computational cost. The generative network may iteratively learn a function that synthetic data with a goal of generating refined synthetic data that is more difficult for the discriminative network to differentiate from real data, while the discriminative network may be configured to iteratively learn a function that classifies data as either synthetic or real. Over multiple iterations, the generative network may learn to refine the synthetic data to produce refined synthetic data on which other machine learning models may be trained.

A follow-up shooting method and device, a medium and an electronic device are provided. The follow-up shooting method includes: acquiring a video image in a live classroom, wherein the video image comprises at least one object; analyzing the video image to obtain a gesture image for controlling a camera; and determining whether to control the camera to track and shoot an execution object related to the gesture image based on the gesture image.

A control device associated with an autonomous vehicle detects that an emergency personnel is altering traffic on a road using an emergency-related hand signal to divert the traffic from a road anomaly, such as a road accident. The control device determines an interpretation of the emergency-related hand signal. The control device determines a proposed trajectory for the autonomous vehicle according to the interpretation of the emergency-related hand signal. In certain embodiments, the control device may navigate the autonomous vehicle according to the interpretation of the emergency-related hand signal. In certain embodiments, the control device may transmit the proposed trajectory to an oversight server for confirmation. In certain embodiments, the oversight may confirm or override the proposed trajectory.

The present invention provides a measurement program selection assisting apparatus capable of visually confirming whether a selected measurement program is suitable for an object to be measured. One aspect of the present invention is a measurement program selection assisting apparatus comprising: a measurement program database storing a measurement program related to measurement of an object and superimposed display information corresponding to a three-dimensional shape of the object in association with each other; a display unit capable of displaying information defined in a virtual space superimposed on the real space; and a display control unit for acquiring the superimposed display information corresponding to a selected measurement program from the measurement program database and displaying the acquired superimposed display information in a mixed reality on the display unit.

An augmented reality (AR) device can be configured to generate a virtual representation of a user's physical environment. The AR device can capture images of the user's physical environment to generate a mesh map. The AR device can project graphics at designated locations on a virtual bounding box to guide the user to capture images of the user's physical environment. The AR device can provide visual, audible, or haptic guidance to direct the user of the AR device to look toward waypoints to generate the mesh map of the user's environment.

A waveguide apparatus includes a planar waveguide and at least one optical diffraction element (DOE) that provides a plurality of optical paths between an exterior and interior of the planar waveguide. A phase profile of the DOE may combine a linear diffraction grating with a circular lens, to shape a wave front and produce beams with desired focus. Waveguide apparati may be assembled to create multiple focal planes. The DOE may have a low diffraction efficiency, and planar waveguides may be transparent when viewed normally, allowing passage of light from an ambient environment (e.g., real world) useful in AR systems. Light may be returned for temporally sequentially passes through the planar waveguide. The DOE(s) may be fixed or may have dynamically adjustable characteristics. An optical coupler system may couple images to the waveguide apparatus from a projector, for instance a biaxially scanning cantilevered optical fiber tip.

Provided are methods and systems for providing a game based on a video call and object recognition. A game providing method of an electronic device configured as a computer including displaying game content on a video call screen in response to a video call being established between a user of the electronic device and a counterpart through a voice over Internet protocol (VoIP) channel, recognizing an object in a call image as motion data with respect to the game content, and controlling a game element corresponding to the game content using a result of the recognizing may be provided.

This disclosure provides a method and a system for detecting and recognizing a target object in a real-time video. The method includes: determining whether a target object recognition result R.sub.X-1 of a previous frame of image of a current frame of image is the same as a target object recognition result R.sub.X-2 of a previous frame of image of the previous frame of image; performing target object position detection in the current frame of image by using a first-stage neural network to obtain a position range C.sub.X of a target object in the current frame of image when the two recognition results R.sub.X-1 and R.sub.X-2 are different; or determining a position range C.sub.X of a target object in the current frame of image according to a position range C.sub.X-1 of the target object in the previous frame of image when the two recognition results R.sub.X-1 and R.sub.X-2 are the same; and performing target object recognition in the current frame of image according to the position range C.sub.X by using a second-stage neural network. Therefore, the operating frequency of the first-stage neural network used for position detection is reduced, the recognition speed is accelerated, and the usage of CPU and internal memory resources is reduced.

An imaging control apparatus includes an identification unit that identifies an object to be introduced and an object introducer who introduces the object to be introduced individually as subjects on the basis of captured image data obtained by an imaging unit of an imaging device, a selection unit that selects one of the object to be introduced and the object introducer as a target subject on the basis of a positional relationship between at least any two of the object to be introduced, the object introducer, and the imaging device, and an imaging control unit that performs imaging control on a subject selected as the target subject by the selection unit.