METHODS AND SYSTEMS OF SPATIOTEMPORAL PATTERN RECOGNITION FOR VIDEO CONTENT DEVELOPMENT

• US 20170255829A1
• Filed: 05/19/2017
• Published: 09/07/2017
• Est. Priority Date: 02/28/2014
• Status: Abandoned Application

• Current Assignee: Genius Sports SS LLC
• Original Assignee: Second Spectrum, Inc. (Genius Sports Ltd.)
• Inventors: Chang, Yu-Han, Maheswaran, Rajiv, Su, Jeffrey Wayne, Hollingsworth, Noel Grant
• Application Number:
  US15/600,404
• Publication Number:
  US 20170255829A1
• Time in Patent Office: Days
• CPC Class Codes: A63F 13/60 Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor
• CPC Class Codes: G06F 3/012 Head tracking input arrangements
• CPC Class Codes: G06F 3/013 Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30221 Sports video; Sports image
• CPC Class Codes: G06V 20/42 of sport video content
• CPC Class Codes: G06V 20/46 Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
• CPC Class Codes: G11B 27/031 Electronic editing of digitised analogue information signals, e.g. audio or video signals
• CPC Class Codes: G11B 27/28 by using information signals recorded by the same method as the main recording G11B27/22 takes precedence
• CPC Class Codes: H04N 13/117 the virtual viewpoint locations being selected by the viewers or determined by viewer tracking
• CPC Class Codes: H04N 13/204 using stereoscopic image cameras stereoscopic photography G03B35/00
• CPC Class Codes: H04N 13/243 using three or more 2D image sensors
• CPC Class Codes: H04N 21/2187 Live feed
• CPC Class Codes: H04N 21/23418 involving operations for analysing video streams, e.g. detecting features or characteristics television picture signal circuitry for scene change detection H04N5/147; filtering for image enhancement G06T5/00; methods or arrangements for recognising scenes G06V20/00; arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• CPC Class Codes: H04N 21/251 Learning process for intelligent management, e.g. learning user preferences for recommending movies details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08
• CPC Class Codes: H04N 21/4223 Cameras H04N23/00 takes precedence
• CPC Class Codes: H04N 21/4345 Extraction or processing of SI, e.g. extracting service information from an MPEG 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 21/4532 : involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4662 : characterized by learning algorithms
• CPC Class Codes: H04N 21/8456 : by decomposing the content in the time domain, e.g. in time segments
• CPC Class Codes: H04N 21/8549 : Creating video summaries, e.g. movie trailer retrieval in video databases by using presentations in form of a video summary G06F16/739
• CPC Class Codes: H04N 5/2224 : related to virtual studio applications

Abstract

A system for enabling user interaction with video content includes an ingestion facility configured to access at least one video feed and a machine learning system configured to process the at least one video feed through a spatiotemporal pattern recognition algorithm that applies machine learning on an event in the at least one feed in order to develop an understanding of the event including identifying context information relating to the event and an entry in a relationship library at least detailing a relationship between two visible video features. The system further includes an extraction facility configured to automatically extract content displaying the event and associate the extracted content with the context information, and a video production facility configured to produce a video content data structure that includes the context information. The system further includes a user interface configured with video interaction options that are based on the context information.

Claims

1. A system for enabling user interaction with video content, comprising:

an ingestion facility executing on at least one processor configured to access at least one video feed;

a machine learning system configured to process the at least one video feed through a spatiotemporal pattern recognition algorithm that applies machine learning on an event in the at least one feed in order to develop an understanding of the event within the at least one video feeds, wherein the understanding includes identifying context information relating to the event and an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed;

an extraction facility configured to automatically, under computer control, extract content displaying the event and associate the extracted content with the context information;

a video production facility configured to produce a video content data structure that includes the context information; and

an application having a user interface configured to permit a user to interact with the video content data structure, wherein the user interface is further configured with options for user interaction that are based on the context information.

2. The system of claim 1, wherein the application is a mobile application.

3. The system of claim 1, wherein the application is at least one of a smart television application, a virtual reality headset application and an augmented reality application.

4. The system of claim 1, wherein the user interface is a touch screen interface.

5. The system of claim 4, wherein the user interface is configured to permit a user to enhance the video feed by selecting a content element to be added to the video feed.

6. The system of claim 5, wherein the content element is at least one of a metric and a graphic element that is based on the understanding developed with the machine learning.

7. The system of claim 1, wherein the user interface is configured to permit the user to select content for a particular player of a sports event.

8. The system of claim 1, wherein the user interface is configured to permit the user to select content relating to a context involving a matchup of two particular players in a sports event.

9. The system of claim 1, wherein the system takes at least two video feeds from different time periods, the machine learning facility determines a context that includes a similarity between at least one of a plurality of players and a plurality of plays in the two feeds, and wherein the user interface is configured to permit the user to select at least one of the players and the plays to obtain a video feed that illustrates a comparison.

10. The system of claim 1, wherein the user interface includes options for at least one of editing, cutting and sharing a video clip that includes the video data structure.

11. The system of claim 1, wherein the at least one video feed comprises 3D motion camera data captured from a live sports venue.

12. The system of claim 1, wherein the machine learning facility increases its ability to develop the understanding by ingesting a plurality of events for which context has already been identified.

13. The system of claim 1, wherein using machine learning to develop the understanding of the event further comprises using events in position tracking data over time obtained from at least one of the at least one video feed and a chip-based player tracking system and wherein the understanding is based on at least two of spatial configuration, relative motion, and projected motion of at least one of a player and an item used in a game.

14. The system of claim 1, wherein using the machine learning to develop the understanding of the event further comprises aligning multiple unsynchronized input feeds related to the event using at least one of a hierarchy of algorithms and a hierarchy of human operators, wherein the unsynchronized input feeds are selected from the group consisting of one or more broadcast video feeds of the event, one or more feeds of tracking video for the event, and one or more play-by-play data feeds of the event.

15. The system of claim 14, wherein the multiple unsynchronized input feeds include at least three feeds selected from at least two types related to the event.

16. The system of claim 14, further comprising at least one of validating and modifying the alignment of the unsynchronized input feeds using a hierarchy involving at least two of one or more algorithms, one or more human operators, and one or more input feeds.

17. The system of claim 1, further comprising at least one of validating the understanding and modifying the understanding using a hierarchy involving at least two of one or more algorithms, one or more human operators, and one or more input feeds.

18. The system of claim 11, further comprising automatically developing a semantic index of a video feed based on the machine understanding of the event in the video feed to indicate a time of the event in the video feed and a location of a display of the event in the video feed.

19. The system of claim 18, wherein the location of the display of the event in the video feed includes at least one of a pixel location, a voxel location, a raster image location.

20. The system of claim 18, further comprising providing the semantic index of the video feed with the video feed to enable augmentation of the video feed.

21. The system of claim 20, wherein augmentation of the video feed includes adding content based on to the location of the display and enabling at least one of a touch interface feature and a mouse interface feature based on the identified location.

22. The system of claim 1, wherein extracting the content displaying the event includes automatically extracting a cut from the at least one video feed using a combination of the understanding developed with the machine learning and an understanding developed with the machine learning of another input feed selected from the group consisting of a broadcast video feed, an audio feed, and a closed caption feed.

23. The system of claim 22, wherein the understanding developed with machine learning of the other input feed includes at least one of a portion of content of a broadcast commentary and a change in camera view in the input feed.

24. A method for enabling a mobile application allowing user interaction with video content, comprising:

taking at least one video feed;

processing the at least one video feed through a spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of an event within the at least one video feed, wherein the understanding includes identifying context information relating to the event and an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed;

automatically, under computer control, extracting content displaying the event and associating the extracted content with the context information;

producing a video content data structure that includes the context information; and

providing a mobile application having a user interface configured to permit a user to interact with the video content data structure, wherein the user interface is configured to include options for user interaction based on the context information.

25. The method of claim 24, wherein the user interface is a touch screen interface.

26. The method of claim 25, wherein the user interface is configured to permit a user to enhance the video feed by selecting a content element to be added to the video feed.

27. The method of claim 26, wherein the content element is at least one of a metric and a graphic element that is based on the machine understanding.

28. The method of claim 24, wherein the user interface is configured to permit the user to select content for a particular player of a sports event.

29. The method of claim 24, wherein the user interface is configured to permit the user to select content relating to a context involving the matchup of two particular players in a sports event.

30. The method of claim 24, further comprising taking at least two video feeds from different time periods, the machine learning facility determines a context the includes a similarity between at least one of a plurality of players and a plurality of plays in the at least two feeds and the user interface is configured to permit the user to select at least one of the players and the plays to obtain a video feed that illustrates a comparison.

31. The method of claim 24, wherein the user interface includes options for at least one of editing, cutting and sharing a video clip that includes the video data structure.

32. The method of claim 24, wherein the video feed comprises 3D motion camera data captured from a live sports venue.

33. The method of claim 24, wherein the machine learning facility increases its ability to develop the understanding by ingesting a plurality of events for which context has already been identified.

34. The method of claim 24, wherein using the machine learning to develop the understanding of the event further comprises using events in position tracking data over time obtained from at least one of the at least one video feed and a chip-based player tracking system, and wherein the understanding is based on at least two of spatial configuration, relative motion, and projected motion of at least one of a player and an item used in a game.

35. The method of claim 24, wherein using the machine learning to develop the understanding of the event further comprises aligning multiple unsynchronized input feeds related to the event using at least one of a hierarchy of algorithms and a hierarchy of human operators, wherein the unsynchronized input feeds are selected from the group consisting of one or more broadcast video feeds of the event, one or more feeds of tracking video for the event, and one or more play-by-play data feeds of the event.

36. The method of claim 35, wherein the multiple unsynchronized input feeds include at least three feeds selected from at least two types related to the event.

37. The method of claim 35, further comprising at least one of validating and modifying the alignment of the unsynchronized input feeds using a hierarchy involving at least two of one or more algorithms, one or more human operators, and one or more input feeds.

38. The method of claim 24, further comprising at least one of validating and modifying the understanding using a hierarchy involving at least two of one or more algorithms, one or more human operators, and one or more input feeds.

39. The method of claim 24, further comprising automatically developing a semantic index of a video feed based on the understanding developed with the machine learning of at least one event in the video feed to indicate a time of the event in the video feed and a location of a display of the event in the video feed.

40. The method of claim 39, wherein the location of the display of the event in the video feed includes at least one of a pixel location, a voxel location, a raster image location.

41. The method of claim 39, further comprising providing the semantic index of the video feed with the video feed to enable augmentation of the video feed.

42. The method of claim 41, wherein augmentation of the video feed includes adding content based on to the location of the display and enabling at least one of a touch interface feature and a mouse interface feature based on the identified location.