Embodiments are directed to a smart camera device that analyzes independent video streams.

Aspects of the subject disclosure may include, for example, receiving data associated with a video, applying the data to a machine learning model to obtain a prediction regarding an accuracy of an assertion included in the video, responsive to the prediction indicating that the assertion is accurate, transmitting the video to a processing system including a processor, and responsive to the prediction indicating that the assertion in inaccurate, preventing a transmission of the video to the processing system. Other embodiments are disclosed.

An apparatus includes an interface, display, memory, and processor. The interface receives a video feed including first and second camera feeds, each feed corresponding to a camera located in a store. The processor stores a video segment in memory, assigned to a person and capturing a portion of a shopping session. The video segment includes first and second camera feed segments, each segment corresponding to a recording of the corresponding camera feed from a starting to an ending timestamp. Playback of the first and second camera feed segments is synchronized, and a slider bar controls a playback progress of the camera feed segments. The processor displays the camera feed segments and copies of the slider bar on the display. The processor receives an instruction from at least one of the copies of the slider bar to adjust the playback progress of the camera feed segments and adjusts the playback progress.

A system and method for identifying media segments using audio augmented image cross-comparison is disclosed, in which a media segment identifying system analyses both audio and video content, producing a unique identifier to compare with previously identified media segments in a media segment database. The characteristic landmark-linked-image-comparisons are constructed by first identifying an audio landmark. The audio landmark is an audio peak that exceeds a predetermined threshold. Two digital images are then obtained, one associated directly with the audio landmark, and one obtained a predetermined landmark time removed from the first image. The two images are then used to provide a characteristic landmark-linked-image-comparison. The pair of images are reduced in pixel size and converted to gray scale. Corresponding pixels are compared to form a numeric comparison. One image is mirrored before comparison to reduce the possibility of null comparisons.

A video management system (VMS) may search for one or more objects and/or events in one or more video streams, and may receive time-stamped metadata that may identify one or more objects and/or events occurring in the corresponding video stream as well as an identifier that uniquely identifies the corresponding video stream. A user may enter a query into a video query engine, wherein the video query engine includes one or more cognitive models. The VMS may apply the search query to the time-stamped metadata via the video query engine to search for one or more objects and/or events in the one or more video streams that match the search query, and returning a search result to the user.

Aspects relate to apparatuses and methods for parsing and comparing resume video duplications. An exemplary apparatus includes a processor configured to acquire a plurality of video elements from a target video resume, wherein the target video resume includes at least an image component, compare at least an existing video resume and the target video resume to obtain a comparison result, and determine, as a function of the comparison result, a duplication coefficient for the target resume video, wherein the duplication coefficient relates to a similarity between the target video and at least and existing resume video.

In one embodiment, a processing device receives a first digital fingerprint of a media item along with a first content management rule. The processing device separately receives a second digital fingerprint of the media item along with a second content management rule. The processing device determines that the received digital fingerprints are for the same media item based upon a match between the first digital fingerprint and the second digital fingerprint. The processing device determines that all rights to the media item have been accounted for, and then determines a set of actions to be performed for hosted media items comprising the media item based at least in part upon the first content management rule and the second content management rule. Processing logic may also perform a conflict resolution process for conflicting rights claims to the media item.

In one aspect, a method includes identifying a group of multiple content-presentation devices that are tuned to the same channel and that are each scheduled to perform, at a modification start-time, a respective content-modification operation that comprises modifying a modifiable content-segment in connection with an upcoming content-modification opportunity on the channel. The method also includes determining that, after the modification start-time, at least a subgroup of the group of content-presentation devices have detected a mismatch between reference fingerprint data representing the modifiable content-segment and query fingerprint data representing content received by at least the subgroup of content-presentation devices. The method also includes determining that at least the subgroup of content-presentation devices are connected to tuner devices associated with the same content distributor and, in response to determining that at least the subgroup of content-presentation devices are connected to tuner devices associated with the same content distributor, performing an action.

The present subject matter discloses an action recognition method, apparatus and device, a storage medium, and a computer program product, belonging to the field of image recognition. Multiple video frames in a target video are obtained. Feature extraction is performed on the multiple video frames respectively according to multiple dimensions to obtain multiple multi-channel feature patterns. Each video frame corresponds to one multi-channel feature pattern. Each channel represents one dimension. An attention weight of each multi-channel feature pattern is determined based on a similarity between every two multi-channel feature patterns. The attention weight is used for representing a degree of correlation between a corresponding multi-channel feature pattern and an action performed by an object in the target video. A type of the action is determined based on the multiple multi-channel feature patterns and the determined multiple attention weights.

A video clip location technology in the field of computer vision pertaining to artificial intelligence that provides a video processing method and apparatus. The method includes: obtaining a semantic feature of an input sentence; performing semantic enhancement on a video frame based on the semantic feature to obtain a video feature of the video frame, where the video feature includes the semantic feature; and determining, based on the semantic feature and the video feature, whether a video clip to which the video frame belongs is a target video clip corresponding to the input sentence. The method helps improve accuracy of recognizing a target video clip corresponding to an input sentence.