The present invention provides a shaft rotational speed measurement device and method based on variable density sinusoidal fringe pattern. The device comprises a variable density sinusoidal fringe pattern sensor, a high speed image acquisition and transmission module, a computer, and an image processing software module. The method comprises the following steps: make the variable density sinusoidal fringe pattern sensor attached on the circumferential surface of the measured shaft, the variable density sinusoidal fringe pattern sensor is continuously imaged and recorded by the high speed image acquisition module, the image transmission module transfers the fringe pattern signal to the computer, the image processing software module carries out Fourier transform to the fringe pattern signal in the same position of each frame, and corrects the peak frequency accurately by using the peak frequency correction method to obtain the accurate fringe pattern density information of each frame, obtains the time domain curve of the rotational angular velocity of the measured shaft, and then calculate the rotational speed of the measured shaft through the rotational angular velocity and sampling frequency. The present invention can realize non-contact measurement of rotational speed of measured shaft within a certain speed range, and the measuring device is simple, the measuring method is fast and accurate.

A computer-implemented visual interface for identifying and revealing objects from video-based media provides visual cues to enable users to interact with video-based media. Objects in videos are inferred and identified based upon automatic interpretations of the video and/or audio that is associated with the video. The automatic interpretations may be performed by a computer-implemented neural network. The computer-implemented visual interface is integrated with the video to enable users to interact with the identified objects. User interactions with the visual interface may be through either touch or non-touch means. Information is delivered to users that is based upon the identified objects, including in augmented or virtual reality-based form, responsive to user interactions with the computer-implemented visual interface.

A computer-implemented visual interface for identifying and revealing objects from video-based media provides visual cues to enable users to interact with video-based media. Objects in videos are inferred and identified based upon automatic interpretations of the video and/or audio that is associated with the video. The automatic interpretations may be performed by a computer-implemented neural network. The computer-implemented visual interface is integrated with the video to enable users to interact with the identified objects. User interactions with the visual interface may be through either touch or non-touch means. Information is delivered to users that is based upon the identified objects, including in augmented or virtual reality-based form, responsive to user interactions with the computer-implemented visual interface.

An information processing device according to the present disclosure includes: an acquisition unit that acquires outline information indicating an outline of a user who makes a body motion; and a specification unit that specifies, among body parts, a main part corresponding to the body motion and a related part, which is to be a target of correction processing of motion information corresponding to the body motion, on the basis of the outline information acquired by the acquisition unit.

A computer device extracts a plurality of target windows from a target video. Each of the target windows comprises a respective plurality of consecutive video frames. For each of the target windows, the device performs action recognition on the respective plurality of consecutive video frames corresponding to the target window to obtain respective first action feature information of the target window. The device obtains a similarity between the first action feature information of the target window and preset feature information. The device determines, from the respective obtained similarities corresponding to the plurality of target windows, a highest first similarity and a first target window corresponding to the highest first similarity. The device also determines a dynamic action corresponding to the highest first similarity as the preset dynamic action in accordance with threshold settings.

A computer device extracts a plurality of target windows from a target video. Each of the target windows comprises a respective plurality of consecutive video frames. For each of the target windows, the device performs action recognition on the respective plurality of consecutive video frames corresponding to the target window to obtain respective first action feature information of the target window. The device obtains a similarity between the first action feature information of the target window and preset feature information. The device determines, from the respective obtained similarities corresponding to the plurality of target windows, a highest first similarity and a first target window corresponding to the highest first similarity. The device also determines a dynamic action corresponding to the highest first similarity as the preset dynamic action in accordance with threshold settings.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating object interaction predictions using a neural network. One of the methods includes obtaining a sensor input derived from data generated by one or more sensors that characterizes a scene. The sensor input is provided to an object interaction neural network. The object interaction neural network is configured to process the sensor input to generate a plurality of object interaction outputs. Each respective object interaction output includes main object information and interacting object information. The respective object interaction outputs corresponding to the plurality of regions in the sensor input are received as output of the object interaction neural network.

Provided are a map construction method, a relocalization method, and an electronic device. The map construction method includes: acquiring a target keyframe, performing feature extraction on the target keyframe to obtain feature point information of the target keyframe, and determining semantic information corresponding to the feature point information of the target keyframe; acquiring feature point information of a previous keyframe of the target keyframe and semantic information corresponding to the feature point information of the target keyframe; determining a feature matching result of a matching of the semantic information and a matching of the feature point information between the target key frame and the previous key frame; and constructing a map based on the feature matching result.

Provided are a map construction method, a relocalization method, and an electronic device. The map construction method includes: acquiring a target keyframe, performing feature extraction on the target keyframe to obtain feature point information of the target keyframe, and determining semantic information corresponding to the feature point information of the target keyframe; acquiring feature point information of a previous keyframe of the target keyframe and semantic information corresponding to the feature point information of the target keyframe; determining a feature matching result of a matching of the semantic information and a matching of the feature point information between the target key frame and the previous key frame; and constructing a map based on the feature matching result.

Systems, methods, and other embodiments for selecting, enriching and sequencing digital media content to produce a narrative-oriented, ordered sub-collection of media such as for movie creation. The method identifies, evaluates, assesses, stores, enriches, groups, and sequences content. The method identifies the content metadata. When metadata are missing or anomalous, the method attempts to populate or correct the metadata and store that new content in the database. The method evaluates content for focus quality and may exclude content based on rules. The method assesses the content storing the people and their emotional level, animals, objects, locations, landmarks and date/time in the database. The method can then enrich the remaining content by providing map, photo, video, text, and audio content. The method uses selecting criteria for grouping and sequencing content by date, time, person, etc. and compiling the sequenced groups into the final narrative ready for distribution, e.g., movie creation.