VIDEO MANAGEMENT SYSTEM FOR PROVIDING VIDEO MANAGEMENT OPERATIONS BASED ON VIDEO CREDITS SEGMENT DETECTION IN VIDEO

• US 20200137456A1
• Filed: 10/24/2018
• Published: 04/30/2020
• Est. Priority Date: 10/24/2018
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: HAUON, Edan, JASSIN, Ohad, NURIELI, Daniel
• Application Number:
  US16/169,994
• Publication Number:
  US 20200137456A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 18/24155 Bayesian classification
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: H04N 21/435 Processing of additional data, e.g. decrypting of additional data, reconstructing software from modules extracted from the transport stream
• CPC Class Codes: H04N 21/44008 involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/47202 for requesting content on demand, e.g. video on demand
• CPC Class Codes: H04N 21/8133 specifically related to the content, e.g. biography of the actors in a movie, detailed information about an article seen in a video program
• CPC Class Codes: H04N 21/8456 by decomposing the content in the time domain, e.g. in time segments

Abstract

Various methods and systems for performing video management operations based on video credits segment detection. Video management systems include different types of video-on-demand (“VOD”) providers that manage videos using the VOD system. In operation, a video credits segment detection machine-learning system is accessed. The video credits segment detection machine-learning system operates based on a video credits segment detection model that supports determining video credits segment detection scores that indicate a likelihood that segments of videos are video credits segments. The video credits segment detection model is generated based on a plurality of video credits segment detection features. A segment of a video is accessed. Using the video credits segment detection model, a video credits segment detection score for the segment is automatically determined. Based on the video credits segment detection score, a video management operation is executed to instruct on functionality available on the video management engine or video client.

Claims

1. A computer-implemented for performing video management operations based on video credits segment detection, the method comprising:

accessing a video credits segment detection machine-learning system, the video credits segment detection machine-learning system operates based on a video credits segment detection model that supports determining video credits segment detection scores that indicate a likelihood that segments of videos are video credits segments of the videos, wherein the video credits segment detection model is generated based on a plurality of video credits segment detection features;

accessing a segment of a video;

using the video credits segment detection model, automatically determining a video credits segment detection score for the segment of the video; and

based on the video credits segment detection score, executing a video management operation.

2. The method of claim 1, wherein the plurality of video credits segment detection features comprise explicitly and implicitly identified characteristics of single frames comprising video credits or multiple frames comprising video credits that support defining the video credits segment detection model as a predictive model that determines whether or not the segment of the video is a video credits segment.

3. The method of claim 2, wherein the explicitly and implicitly identified characteristics of single frames comprising video credits or multiple frames comprising video credits are associated with rolling video credits that scroll from a bottom portion to a top portion of a video display.

4. The method of claim 2, wherein the plurality of video credits segment features includes a first feature associated with an extracted sub-feature, wherein the extracted sub-feature further defines the video credits segment detection model, wherein the first feature is a subtitle file having the extracted sub-feature.

5. The method of claim 1, wherein the plurality of video credits segment detection features are associated with single frame features, wherein the segment of the video is a single frame that is given the video credits segment detection score to classify the segment of the video as a video credits segment;

and

wherein the plurality of video credits segment detection features are associated with multiple frames video credits features, wherein the segment of the video is multiple frames that is given the video credits segment detection score to classify the segment of the video as a video credits segment.

6. The method of claim 1, wherein the accessing the segment of the video comprising access a subsection of the video, wherein the subsection of the video is an end portion of the video that is iteratively processed to determine a starting point of video credits in the video.

7. The method of claim 1, wherein executing the video management operation comprises executing at least one of the following:

a first video management operation that instructs on functionality on a video management system of the video;

a second video management operation that instructs on functionality on a video client accessing the video; and

a third video management operation that instructs on functionality on both the video management engine of the video and the video client accessing the video.

8. One or more computer storage media having computer-executable instructions embodied thereon that, when executed, by one or more processors, cause the one or more processors for performing video management operations based on video credits segment detection, the method comprising:

accessing a video credits segment detection machine-learning system, the video credits segment detection machine-learning system operates based on a video credits segment detection model that supports determining video credits segment detection scores that indicate a likelihood that segments of videos are video credits segments of the videos, wherein the video credits segment detection model is generated based on a plurality of video credits segment detection features;

accessing a first segment of a video, wherein the first segment is a single frame of the video;

using the video credits segment detection model, automatically determining a first video credits segment detection score for the first segment of the video;

based on the first video credits segment detection score, accessing a second segment of the video, wherein the second segment is multiple frames of the video comprising the first segment of the video;

using the video credits segment detection model, automatically determining a second video credits segment detection score for the second segment of the video; and

based on the second video credits segment detection score, executing a video management operation.

9. The media of claim 8, wherein the plurality of video credits segment detection features comprise explicitly and implicitly identified characteristics of single frames comprising video credits or multiple frames comprising video credits that support defining the video credits segment detection model as a predictive model that determines whether or not the segment of the video is a video credits segment,

wherein the plurality of video credits segment features includes a first feature associated with an extracted sub-feature, wherein the extracted sub-feature further defines the video credits segment detection model.

10. The media of claim 8, wherein the accessing the first segment of the video comprises accessing a subsection of the video, wherein the subsection of the video is an end portion.

11. The media of claim 8, wherein the first video credits segment detection score for the segment of the video meets a threshold video credits segment detection score that triggers accessing the second segment of the video comprising multiple frames including the first segment of the video.

12. The media of claim 8, wherein the plurality of video credits segment detection features are associated with single frame features, wherein the segment of the video is a single frame that is given the video credits segment detection score to classify the segment of the video as a video credits segment;

or

wherein the plurality of video credits segment detection features are associated with multiple frames video credits features, wherein the segment of the video is multiple frames that is given the video credits segment detection score to classify the segment of the video as a video credits segment.

13. The media of claim 8, wherein executing the video management operation comprises causing generation of a video credit segment indicator icon on a progress bar on a video client accessing the video, wherein the video credit segment indicator icon is generated on the progress location proximate a video credits segment of the video.

14. The media of claim 8, wherein executing the video management operation comprises causing generation of a prompt requesting an input indicating whether or not the current video segment is a video credits segment of the video.

15. A video management system for performing video management operations based on video credits segment detection, the system comprising:

one or more processors; and

one or more computer storage media storing computer-useable instructions that, when used by the one or more processors, cause the one or more processors to execute;

a video management engine;

accessing a video credits segment detection machine-learning system, the video credits segment detection machine-learning system operates based on a video credits segment detection model that supports determining whether or not segments of videos are video credits segments of the videos, wherein the video credits segment detection model is generated based on a plurality of video credits segment detection features;

accessing a segment of a video;

using the video credits segment detection model, automatically determine whether or not the segment of the video is a video credits segment of the video; and

based on determining that the segment of the video is a video credits segment of the video, executing a video management operation for a video client accessing the video.

16. The system of claim 15, wherein determining whether or not segments of videos are video credits segments of the videos is based on video credits segment detection scores that indicate a likelihood that segments of videos are video credits segments of the videos.

17. The system of claim 15, wherein executing the video management operation comprises providing a prompt requesting an input indicating whether or not a current video segment of the video is a video credits segment of the video;

and

based on receiving the input indicating that the current video segment of the video is or is not a video credits segment of the video, executing another video management operation.

18. The system of claim 15, further comprising a video credits segment detection machine-learning system configured to:

train the video credits segment detection model, based on the plurality of video credits segment detection features, to receive the segment as a single frame or as multiple frames and generate an output; and

wherein the plurality of video credits segment detection features comprise explicitly and implicitly identified characteristics of single frames comprising video credits or multiple frames comprising video credits that support defining the video credits segment detection model as a predictive model that determines whether or not the segment of the video is a video credits segment.

19. The system of claim 15, wherein the plurality of video credits segment detection features are associated with single frame features, wherein the segment of the video is a single frame that is given the video credits segment detection score to classify the segment of the video as a video credits segment;

or

wherein the plurality of video credits segment detection features are associated with multiple frames video credits features, wherein the segment of the video is multiple frames that is given the video credits segment detection score to classify the segment of the video as a video credits segment.

20. The system of claim 15, the system further comprising a video management operations engine configured to execute the video management operation, wherein executing the video management operations comprises executing at least one of the following:

a first video management operation that instructs on functionality on the video management engine of the video;

a second video management operation that instructs on functionality on the video client accessing the video; and

a third video management operation that instructs on functionality on both the video management engine of the video and the video client accessing the video.