Example systems, devices, media, and methods are described for controlling one or more virtual elements on a display in response to hand gestures detected by an eyewear device that is capturing frames of video data with its camera system. An image processing system detects a hand and presents a menu icon on the display in accordance with a detected current hand location. The image processing system detects a series of hand shapes in the captured frames of video data and determines whether the detected hand shapes match any of a plurality of predefined hand gestures stored in a hand gesture library. In response to a match, the method includes executing an action in accordance with the matching hand gesture. In response to an opening gesture, an element animation system presents one or more graphical elements incrementally moving along a path extending away from the menu icon. A closing hand gesture causes the elements to retreat along the path toward the menu icon.

An apparatus includes a sensor module. The sensor module includes an electromagnetic radiation sensor configured to provide electromagnetic radiation sensor data. The sensor module further includes a coded mask configured to modulate electromagnetic radiation incident to the electromagnetic radiation sensor and from which the electromagnetic radiation sensor data is generated. The apparatus further includes a computation module configured to obtain the electromagnetic radiation sensor data from the electromagnetic radiation sensor. The computation module is further configured to detect a property from the electromagnetic radiation sensor data using an artificial neural network. The computation module is further configured to output information related to the detected property via an output.

Provided is a gesture detection apparatus that accurately detects a hand of an occupant in gesturing. The gesture detection apparatus includes a face frame information acquisition unit, a hand candidate detection unit, and a determination unit. The face frame information acquisition unit acquires face frame information. The face frame is set so as to surround the face of the occupant detected on the basis of the video. The hand candidate detection unit detects a hand candidate on the basis of the video. The determination unit rejects the information of the hand candidate so that the hand candidate is not detected as the hand of the occupant in the gesture of the occupant as a detection target on the basis of a predetermined condition regarding the overlap between the face frame in the video and the hand candidate frame set to surround the hand candidate.

There is provided a recognition system adaptable to a portable device or a wearable device. The recognition system senses a body heat using a thermal sensor, and performs functions such as the living body recognition, image denoising and body temperature prompting according to detected results.

Stroke special effect program file package generating methods and apparatuses, and stroke special effect generating methods and apparatuses are provided. The stroke special effect program file package generating method includes: obtaining a parameter value of a stroke effect parameter of a target object; and generating a stroke special effect program file package according to the parameter value of the stroke effect parameter of the target object.

The embodiments of the disclosure provide a method and a system for detecting a hand gesture, and a computer readable storage medium. The method includes: determining whether information of a hand is enough for identifying a hand gesture of the hand; in response to determining that the information of the hand is enough for identifying the hand gesture, identifying the hand gesture, receiving first hand gesture information from at least one external gesture information provider, and correcting the hand gesture based on the first hand gesture information; in response to determining that the information of the hand is not enough for identifying the hand gesture, receiving second hand gesture information from the at least one external gesture information provider, obtaining a predicted hand gesture, and obtaining the hand gesture based on the predicted hand gesture and the second hand gesture information.

A method of operating a wearable electronic device includes: recognizing a first gesture of a hand of a user for setting a region of interest (ROI) corresponding to a view of the user in an image frame corresponding to a view of a camera; generating a virtual display for projecting the ROI based on whether the first gesture is recognized; extracting the ROI from the image frame; recognizing a second gesture of the hand for adjusting a size of the ROI; and adjusting the size of the ROI and projecting the ROI of the adjusted size onto the virtual display, based on whether the second gesture is recognized.

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 mobility surrogate includes a humanoid form supporting at least one camera that captures image data from a first physical location in which the first mobility surrogate is disposed to produce an image signal and a mobility base. The mobility base includes a support mechanism, with the humanoid form affixed to the support on the mobility base and a transport module that includes mechanical drive mechanism and a transport control module including a processor and memory that are configured to receive control messages from a network and process the control messages to control the transport module according to the control messages received from the network.

A method for operating a distributed neural network having a plurality of intelligent devices and a server includes: generating, by a first intelligent device of the plurality of intelligent devices, a first output using a first neural network model running on the first intelligent device and using a first input vector to the first neural network model; outputting, by the first intelligent device, the first output; receiving, by the first intelligent device, a gesture feedback on the first output from a user; determining, by the first intelligent device, a user rating of the first output from the gesture feedback; labeling, by the first intelligent device, the first input vector with a first label in accordance with the user rating; and training, by the first intelligent device, the first neural network model using the first input vector and the first label.