A system for detecting errors in task workflows from a real time video feed records. The video feed that shows a plurality of steps being performed to accomplish a plurality of tasks through an automation process system. The system splits the video feed into a plurality of video recordings which are valid breakpoints determined through cognitive Machine Learning Engine, where each video recording shows a single task. For each task from among the plurality of tasks, the system determines whether the task fails and the exact point of failure for that task. If the system determines that the task fails, the system determines a particular step where the task fails. The system flags the particular step as a failed step. The system reports the flagged step for troubleshooting.

An apparatus including an interface and a processor. The interface may be configured to receive pixel data generated by a capture device. The processor may be configured to generate video frames in response to the pixel data, perform computer vision operations on the video frames to detect objects, perform a classification of the objects detected based on characteristics of the objects, determine whether the classification of the objects corresponds to a user-defined event and generate encoded video frames from the video frames. The encoded video frames may be communicated to a cloud storage service. The encoded video frames may comprise a first sample of the video frames selected at a first rate when the user-defined event is not detected and a second sample of the video frames selected at a second rate while the user-defined event is detected. The second rate may be greater than the first rate.

A system for detecting errors in task workflows from a real time video feed records. The video feed that shows a plurality of steps being performed to accomplish a plurality of tasks through an automation process system. The system splits the video feed into a plurality of video recordings which are valid breakpoints determined through cognitive Machine Learning Engine, where each video recording shows a single task. For each task from among the plurality of tasks, the system determines whether the task fails and the exact point of failure for that task. If the system determines that the task fails, the system determines a particular step where the task fails. The system flags the particular step as a failed step. The system reports the flagged step for troubleshooting.

Video information defining video content may be accessed. Highlight moments within the video content may be identified. Flexible video segments may be determined based on the highlight moments. Individual flexible video segments may include one or more of the highlight moments and a flexible portion of the video content. The flexible portion of the video content may be characterized by a minimum segment duration, a target segment duration, and a maximum segment duration. A duration allocated to the video content may be determined. One or more of the flexible video segments may be selected based on the duration and one or more of the minimum segment duration, the target segment duration, and/or the maximum segment duration of the selected flexible video segments. A video summary including the selected flexible video segments may be generated.

Disclosed herein is a system and method directed to object tracking and metric generation using a plurality of cameras. The system includes the plurality of cameras disposed around a playing surface in a mirrored configuration, where the plurality of cameras are time-synchronized. The system further includes logic that, when executed by a processor, causes performance of operations including: obtaining a sequence of images from the plurality of cameras, continuously detecting an object in image pairs at successive points in time, wherein each image pair corresponds to a single point in time, continuously determining a location of the object within the playing space through triangulation of the object within each image pair, detecting a player and the object within each image of a subset of image pairs of the sequence of images, identifying a sequence of interactions between the object and the player, and storing the sequence of interactions.

Systems, methods, and devices are disclosed enabling smart search functionalities utilizing key scene changes appearing in video content. In various embodiments, the method includes the step or process of, while engaged in playback of the video content, receiving a user command at a playback device to shift a current playback position of the video content to a default search playback position (PP.sub.DS). In response to receipt of the user command, the playback device searches a time window encompassing the default search playback position (PP.sub.DS) for a key scene change in the video content. If locating a key scene change within the time window, the playback device shifts playback of the video content to a playback position corresponding to the key scene change (PP.sub.ST). Otherwise, the playback device shifts playback of the video content to the default search playback position (PP.sub.DS).

Techniques and systems are provided for generating media content. For example, a server computer can detect a trigger from a device located at a site. The trigger is associated with an event at the site. The server computer can obtain media segments of media captured by a plurality of media capture devices located at the site. At least one of the media segments corresponds to the detected trigger. The server computer can determine one or more quality metrics of a media segment based on a first motion of an object captured in the media segment and/or a second motion of a media capture device used to capture the media segment. A subset of media segments can be selected from the obtained media segments based on quality metrics determined for the obtained media segments. A collection of media segments including the subset of media segments can then be generated.

A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.

An encoding device for video analysis and composition includes circuitry configured to receive an input video having a first data volume, determine at least a region of interest of the input video, and encode at least an output video as a function of the input video and the at least a region of interest, wherein the at least an output video has at least a second data volume, and the at least a second data volume is less than the first data volume.

In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.