METHODS AND SYSTEMS OF SPATIOTEMPORAL PATTERN RECOGNITION FOR VIDEO CONTENT DEVELOPMENT

• US 20170255828A1
• Filed: 05/19/2017
• Published: 09/07/2017
• Est. Priority Date: 02/28/2014
• Status: Active Grant

• 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
• Granted Patent: US 10,755,103 B2
• 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

Interacting with a broadcast video content stream is performed with a machine learning facility that processes a video feed of a video broadcast through a spatiotemporal pattern recognition algorithm that applies machine learning on at least one event in the video feed in order to develop an understanding of the at least one event. Developing the understanding includes identifying context information relating to the at least one event and identifying an entry in a relationship library detailing a relationship between two visible features of the video feed. Interacting is further enabled with a touch screen user interface configured to permit at least one broadcaster to control a portion of the content of the video feed through interaction options that are based on the identified context information. Interacting is further enhanced through an interface configured to permit remote viewers to control the portion of the content.

Claims

1. A system for enabling interaction with a broadcast video content stream, comprising:

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

a touch screen user interface configured to permit at least one broadcaster to interact with the at least one video feed, wherein the touch screen user interface includes options that are based on the context information for controlling a portion of content of the at least one video feed for the video broadcast; and

an interface configured to permit remote viewers to control the portion of the content of the at least one video feed for the video broadcast.

2. The system of claim 1, wherein the touch screen interface is a relatively large screen configured to be seen by viewers of the video broadcast as the broadcaster uses the touch screen interface.

3. The system of claim 2, wherein the touch screen interface is configured to permit the broadcaster to select from a plurality of context-relevant metrics to be displayed on the large screen.

4. The system of claim 1, wherein the touch screen interface is configured to permit the broadcaster to display a plurality of video feeds that have similar contexts as determined by the machine learning facility.

5. The system of claim 4, wherein the similarity of contexts is determined by comparing events within the plurality of video feeds.

6. The system of claim 4, wherein the touch screen interface is configured to permit the broadcaster to display a superimposed view of at least two video feeds to facilitate a comparison of events from the plurality of video feeds.

7. The system of claim 6, wherein the comparison of the events the from the plurality of video feeds is of similar players from different time periods of the video broadcast.

8. The system of claim 7, wherein the understanding developed by the machine learning includes detail regarding similarity of players based on characteristics of the players identified during the different time periods.

9. The system of claim 1, wherein the touch screen interface is configured to permit the broadcaster to display a plurality of highlights that is automatically determined by an understanding developed with the machine learning of a live sports event that is a subject of the at least one video feed.

10. The system of claim 9, wherein the plurality of highlights is determined to be based on similarity to highlights that have been identified for other events.

11. The system of claim 1, wherein using the machine learning to develop the understanding of the at least one 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 developed with the machine learning 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.

12. The system of claim 1, wherein using the machine learning to develop the understanding of the at least one event further comprises aligning multiple unsynchronized input feeds related to the at least one 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.

13. The system of claim 12, wherein the multiple unsynchronized input feeds include at least three feeds selected from at least two types related to the event.

14. The system of claim 12, 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 where at least one algorithm in the hierarchy for validation is based on the nature of the input feed.

15. The system of claim 1, further comprising at least one of validating and modifying the understanding developed with the machine learning using a hierarchy involving at least two of one or more algorithms, one or more human operators, and one or more input feeds, wherein the one or more algorithms in the hierarchy for validation is based on a nature of an input feed.

16. The system of claim 1, further comprising automatically developing a semantic index of the at least one video feed based on the understanding developed with the machine learning of the at least one event in the at least one video feed to indicate at least one time of the at least one event in the at least one video feed and a location of a display of the at least one event in the at least one video feed.

17. The system of claim 16, wherein the location of the display of the at least one event in the at least one video feed includes at least one of a pixel location, a voxel location, a raster image location.

18. The system of claim 16, further comprising providing the semantic index of the at least one video feed with the at least one video feed to enable augmentation of the at least one video feed.

19. The system of claim 18, wherein the enabled augmentation of the at least one video feed includes adding content based on the location of the display, and wherein the touch screen user interface is configured to enable a touch screen feature and a mouse interface feature based on the identified location.

20. A method of enabling interaction with a broadcast video content stream, comprising:

processing a video feed through a spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of an event within the video feed for a video broadcast, 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 video feed; and

providing a touch screen user interface configured to permit a broadcaster to interact with the video feed and configured to permit the broadcaster to select options in the touch screen user interface based on the identifying context information, wherein the options in the touch screen user interface control content of a portion of the video broadcast.

21. The method of claim 20, wherein the touch screen interface is a relatively large screen adapted to be seen by viewers of the video broadcast as the broadcaster uses the touch screen interface.

22. The method of claim 20, wherein the touch screen interface is configured to permit the broadcaster to select from a plurality of metrics relevant to the context information relating to the event and to display the plurality of metrics on the large screen.

23. The method of claim 20, wherein the touch screen interface is configured to permit the broadcaster to display a plurality of video feeds for the video broadcast, wherein the understanding developed with the machine learning includes similar identifying context information in the plurality of video feeds for the video broadcast.

24. The method of claim 23, wherein the similar identifying context information in the plurality of video feeds for the video broadcast is determined by comparing events within the plurality of video feeds.

25. The method of claim 23, wherein the touch screen interface is configured to permit the broadcaster to display a superimposed view of at least two video feeds to facilitate a comparison of events from a plurality of video feeds.

26. The method of claim 25, wherein the comparison of the events from the plurality of video feeds is of similar players from different time periods.

27. The method of claim 26, wherein the understanding developed by the machine learning includes similarity of players based on characteristics of the similar players from the different time periods.

28. The method of claim 20, wherein the understanding developed by the machine learning includes a plurality of highlights based on a live sports event that is a subject of the video feed, and wherein the touch screen interface is configured to permit the broadcaster to display the plurality of highlights.

29. The method of claim 28, wherein the plurality of highlights is determined based on similarity to highlights identified for other events.

30. The method of claim 20, 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 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.

31. The method of claim 20, 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 at least one event.

32. The method of claim 31, wherein the multiple unsynchronized input feeds include at least three feeds selected from at least two types related to the event.

33. The method of claim 31, further comprising at least one of validating and modifying the aligning of the multiple unsynchronized input feeds related to the event using a hierarchy involving at least two of one or more algorithms, one or more human operators, and one or more input feeds.

34. The method of claim 20, 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.

35. The method of claim 20, further comprising automatically developing a semantic index of the video feed based on the understanding developed with the machine learning 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.

36. The method of claim 35, 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.

37. The method of claim 35, further comprising providing the semantic index of the video feed with the video feed to enable augmentation of the video feed.

38. The method of claim 37, 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.