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.

One example method includes capturing audio data at a client engine while outputting an output video, the output video being based upon an original video stored at the client engine, delivering the captured audio data to a prediction engine upon the captured audio data being captured for a pre-determined time, receiving from the prediction engine substitute frame data used by the client engine to stitch one or more frames into the original video stored at the client engine, and following stitching the one or more frames into the output video to generate an altered output video, outputting the captured audio data and the altered video from the client engine.

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.

A system and method for capturing and sharing console gaming data is described. Embodiments capture gameplay data directly at the gaming console, without the need for external hardware. This allows users to easily capture rich console gaming experiences and share them across a variety of outlets. In one embodiment, the methods described herein can be implemented with a patch or driver on the operating system of the user device, rendering it unnecessary to heavily modify the source code of the game.

A device includes a memory configured to store instructions and a processor configured to execute the instructions to obtain image data of a region of interest included in an image frame. The processor may also be configured to compare the image data of the region of interest with image data of a background to detect a change in the region of interest. The processor may further be configured to detect the object in image frame based on the detected change.

A method of generating a moving thumbnail is disclosed. The method includes sampling video frames of a video item. The method further includes determining frame-level quality scores for the sampled video frames. The method also includes determining multiple group-level quality scores for multiple groups of the sampled video frames using the frame-level quality scores of the sampled video frames. The method further includes selecting one of the groups of the sampled video frames based on the multiple group-level quality scores. The method includes creating a moving thumbnail using a subset of the video frames that have timestamps within a range from the start timestamp to the end timestamp.

The various implementations described herein include methods, devices, and systems for detecting motion and persons. In one aspect, a method is performed at a smart home system that includes a video camera, a server system, and a client device. The video camera captures video and audio, and wirelessly communicates, via the server system, the captured data to the client device. The server system: (1) receives and stores the captured data from the video camera; (2) determines whether an event has occurred, including detected motion; (3) in accordance with a determination that the event has occurred, identifies video and audio corresponding to the event; and (4) classifies the event. The client device receives information indicative of the identified events, displays a user interface for reviewing the video and audio stored by the remote server system, and displays the at least one classification for the event.

In one embodiment of the present invention, a native resolution analyzer generates a log-magnitude spectrum that elucidates sampling operations that have been performed on a scene. In operation, the native resolution analyzer performs a transform operation of a color component associated with a frame included in the scene to generate a frame spectrum. The native resolution analyzer then normalizes the magnitudes associated with the frame spectrum and logarithmically scales the normalized magnitudes to create a log-magnitude frame spectrum. This two dimensional log-magnitude frame spectrum serves as a frequency signature for the frame. More specifically, patterns in the log-magnitude spectrum reflect re-sampling operations, such as a down-sampling and subsequent up-sampling, that may have been performed on the frame. By analyzing the log-magnitude spectrum, discrepancies between the display resolution of the scene and the lowest resolution with which the scene has been processed may be detected in an automated fashion.

A user device provides a user interface for video manipulation with face replacement. A method of implementations includes accessing a video comprising a plurality of frames that comprise one or more faces, providing a plurality of stickers comprising alternate face graphics for the one or more faces, receiving, via a user interface of a user device, user selection of one of the stickers and a selected face of the one or more faces, accessing a plurality of face frame sequences of the video, wherein each face frame sequence is a sequence of frames of the video comprising the selected face of the one or more faces, and replacing the selected face with the selected sticker in a first face frame sequence of the plurality of face frame sequences and in a second face frame sequence of the plurality of face frame sequences.

The various embodiments described herein include methods, devices, and systems for providing event alerts. In one aspect, a method includes: (1) obtaining a video feed, the video feed comprising a plurality of images; and, (2) for each image, analyzing the image to determine whether the image includes a person, the analyzing including: (a) determining that the image includes a potential instance of a person by analyzing the image at a first resolution; (b) in accordance with the determination that the image includes the potential instance, denoting a region around the potential instance; (c) determining whether the region includes an instance of the person by analyzing the region at a second resolution, greater than the first resolution; and (d) in accordance with a determination that the region includes the instance of the person, determining that the image includes the person.