Various embodiments of the disclosure provide a method and a device which includes multiple cameras arranged at different positions, multiple microphones arranged at different positions, a memory, and a processor operatively connected to at least one of the multiple cameras, the multiple microphones, and the memory, wherein the processor is configured to: determine, using at least one of the multiple cameras, whether at least one of a user wearing the electronic device or a counterpart having a conversation with the user makes an utterance, configure directivity of at least one of the multiple microphones based on the determination, obtain an audio from at least one of the multiple microphones based on the configured directivity, obtain an image including a mouth shape of the user or the counterpart from at least one of the multiple cameras, and process speech of an utterance target in a different manner based on the obtained audio and the image.

Various embodiments of the disclosure provide a method and a device which includes multiple cameras arranged at different positions, multiple microphones arranged at different positions, a memory, and a processor operatively connected to at least one of the multiple cameras, the multiple microphones, and the memory, wherein the processor is configured to: determine, using at least one of the multiple cameras, whether at least one of a user wearing the electronic device or a counterpart having a conversation with the user makes an utterance, configure directivity of at least one of the multiple microphones based on the determination, obtain an audio from at least one of the multiple microphones based on the configured directivity, obtain an image including a mouth shape of the user or the counterpart from at least one of the multiple cameras, and process speech of an utterance target in a different manner based on the obtained audio and the image.

In one aspect, a computerized method for contextual smart computer vision comprising: with a digital camera of a mobile device, obtaining a digital image of a computer screen, wherein the computer screen is displaying a specified computing application; with a machine learning algorithm: obtaining a set of training digital images of computer screens and associated contextual actions, and using the machine learning algorithm to build and train a machine learning classifier based on the set of training digital images of computer screens and associated contextual actions; and using the machine learning classifier to classify the digital image and determine a specific application action based on the classification of the digital image; and based on context, the mobile application suggests specified contextual actions.

In one aspect, a computerized method for contextual smart computer vision comprising: with a digital camera of a mobile device, obtaining a digital image of a computer screen, wherein the computer screen is displaying a specified computing application; with a machine learning algorithm: obtaining a set of training digital images of computer screens and associated contextual actions, and using the machine learning algorithm to build and train a machine learning classifier based on the set of training digital images of computer screens and associated contextual actions; and using the machine learning classifier to classify the digital image and determine a specific application action based on the classification of the digital image; and based on context, the mobile application suggests specified contextual actions.

A display apparatus may include: a camera; a display; a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory to: analyze an image captured by the camera and detect a free space included in the image; determine an exercise motion performable in the free space, based on the detected free space; and control the display to output exercise content based on the determined exercise motion.

A display apparatus may include: a camera; a display; a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory to: analyze an image captured by the camera and detect a free space included in the image; determine an exercise motion performable in the free space, based on the detected free space; and control the display to output exercise content based on the determined exercise motion.

A computing device equipped with a camera may be used to assist a person in planning and traversing exit routes for a premises. For example, a user may be able to interact with one or more user interfaces generated by the computing device to determine an exit route for the premises. The user may be able to identify various objects, such as stairs or doors, along the exit route. The user may be able to identify graphical or textual information that can be displayed at certain points along the exit route. After determining the exit route, a data structure for the exit route may be shared with other users and/or be used to assist a user in traversing the exit route. For example, the data structure may be used as a basis for overlaying graphics and/or text on a real-time video display as the user traverses the exit route.

An information processing system includes an imaging unit that generates an image signal by imaging and an information processing device. The information processing device performs at least any one of plural kinds of image processing on a taken image corresponding to the image signal. The information processing device specifies an object corresponding to a partial image included in the taken image on the basis of a state of the object corresponding to the partial image included in the taken image or a degree of reliability given to a processing result of the performed image processing.

The present application relates to a pet safety management method and system, computer equipment and a storage medium. The method includes: acquiring a first video comprising a target pet and a target object, the target object being an active object except the target pet; analyzing the first video to determine a first state of the target pet and a second state of the target object; acquiring a surrounding environment video of the target pet if the target pet is determined to be in an initial dangerous state according to the first state and the second state; and analyzing the surrounding environment, determining that the target pet is in a dangerous state if the surrounding environment is an interference-free environment, and controlling an warning device carried by the target pet to send a warning message to timely prevent a pet from being stolen.

This disclosure relates to an information pushing method in a vehicle driving scene. The method may include receiving push information in the vehicle driving scene and obtaining driving scene image information collected by an in-vehicle image collection device. The method may further include identifying scene category identification information based on the driving scene image information. The scene category identification information is for indicating a category of the environmental information. The method may further include pushing, in response to the scene category identification information satisfying a push condition, the push information in the vehicle driving scene.