A method of generating an emotion descriptor icon includes receiving input content comprising video information, and performing analysis on the input content to produce information representing the video information with respect to a plurality of characteristics. The method also includes determining, based on a comparison of the information representing the video information at a temporal position in the video information and a set of information items respectively representing an emotion state, a relative likelihood of association between the input content and at least some of a plurality of emotion states, selecting an emotion state based on the outcome of the determination, and outputting an emotion descriptor icon selected from an emotion descriptor icon set comprising a plurality of emotion descriptor icons. The outputted emotion descriptor icon is associated with the selected emotion state.

A method may include capturing, by a camera, video data that relates to operation of the automobile. The method may include storing the video data using a first data storage device that includes a first storage capacity in which older video data included in the first data storage device is overwritten by newer video data upon exceeding the first storage capacity. The method may include determining whether an event has occurred at a given time point, and responsive to determining that the event has occurred, identifying a video segment included in the first data storage device that corresponds to the event. The method may include storing the video segment using a second data storage device. The method may include identifying a reviewing entity to which the video segment may be sent based on video content of the video segment and sending the video segment to the identified reviewing entity.

A Method, apparatus, device, and storage media for image processing are provided. The method include: acquiring a set of image sequences, the set of image sequences including a plurality of image sequences; determining a first similarity measurement between image sequences in the set of image sequences; dividing the set of image sequences into one or more subset of image sequences based on a first similarity measurement; and determining, in each subset of image sequences, degrees of correlation between images in one image sequence of the subset of image sequences and images in other image sequences of the subset of image sequences.

A scratchpad creation method and an electronic device are disclosed. The method includes: receiving a first input performed by a user on a target identifier, where the target identifier is associated with a first video file; and displaying a first scratchpad in response to the first input, where the first scratchpad is a scratchpad created based on content of the first video file, the first scratchpad includes at least one video identifier and at least one progress identifier, the video identifier is used to indicate a video clip in the first video file, and the progress identifier is used to indicate completion progress of an operation corresponding to the video clip.

Embodiments of the disclosure provide a data processing method and apparatus, a device, and a medium. The method includes: performing video analysis on video data of a target video to obtain a plurality of video segments; determining a video template associated with a target user from a video template database based on a user portrait of the target user, and obtaining at least one predetermined template segment and a template tag sequence in the video template; screening at least one video segment matching the template attribute tag of the at least one template segment; splicing the at least one matched video segment according to a position of a template attribute tag of each template segment in the template tag sequence as a video material segment of the target video; and pushing the video data and the video material segment to an application client corresponding to the target user,

Methods, devices, and systems for segmenting and annotating videos for analysis are disclosed. A user identifies specific moments of the video that provide a teachable moment. A pre-context and a post-context portion of the video surrounding the identified moment are used to create a tile video. One or more tile videos are compiled in a user-defined order to generate a weave video with a specific focus or theme. The generated weave video is shared with one or more users and can be annotated to facilitate teaching and/or discussion.

The inventive concept relates to a method for producing a multi-reactive video and providing a multi-reactive video service, and a program using the same. It is possible to grasp a user's reaction to a video by recording manipulation details for a specific user's multi-reactive video. For example, it is possible to grasp the object of interest and the degree of interest of a user and to grasp a user interest in the user, by grasping the number of touch manipulations to the user's multi-reactive video, a frame in which a touch manipulation has been performed, and an object in the frame, or the like.

A computer-implemented method for sampling and analyzing data from at least one image frame from at least one series of image frames captured by at least one sensor, comprises: defining at least one sampling model, wherein the sampling model is defined in a virtual 3D-vector space and is based on one or more predetermined shapes in the virtual 3D-vector space, applying the at least one sampling model to at least one part of the at least one image frame of the at least one series of image frames, wherein applying of the at least one sampling model defines at least one area of the at least one image frame from which data is to be extracted, extracting data from the at least one area of the at least one image frame defined by the sampling model, and analyzing the extracted data.

A computer-implemented method for classifying video data with improved accuracy includes obtaining, by a computing system comprising one or more computing devices, video data comprising a plurality of video frames; extracting, by the computing system, a plurality of video tokens from the video data, the plurality of video tokens comprising a representation of spatiotemporal information in the video data; providing, by the computing system, the plurality of video tokens as input to a video understanding model, the video understanding model comprising a video transformer encoder model; and receiving, by the computing system, a classification output from the video understanding model.

Disclosed herein are an object recognition apparatus and method based on environment matching. The object recognition apparatus includes memory for storing at least one program, and a processor for executing the program, wherein the program performs extracting at least one key frame from a video that is input in real time, determining a similarity between the key frame extracted from the input video and each of videos used as training data of prestored multiple recognition models, based on a pretrained similarity-matching network, and selecting a recognition model pretrained with a video having a maximal similarity to the key frame extracted from the input video, preprocessing the input video such that at least one of color and size of a video used as training data of an initial model is similar to that of the input video, and recognizing the preprocessed video based on the initial model.