ITERATIVE, MAXIMALLY PROBABLE, BATCH-MODE COMMERCIAL DETECTION FOR AUDIOVISUAL CONTENT

• US 20100281499A1
• Filed: 07/13/2010
• Published: 11/04/2010
• Est. Priority Date: 11/18/1999
• Status: Active Grant

• Current Assignee: Interval Licensing LLC
• Original Assignee: Interval Licensing LLC
• Inventors: Harville, Michael L.
• Granted Patent: US 8,630,536 B2
• Time in Patent Office: Days
• US Class Current: 725/32
• CPC Class Codes: G11B 27/031 Electronic editing of digitised analogue information signals, e.g. audio or video signals
• CPC Class Codes: H04N 21/438 Interfacing the downstream path of the transmission network originating from a server, e.g. retrieving encoded video stream packets from an IP network
• 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 21/812 involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 5/76 Television signal recording
• CPC Class Codes: H04N 5/781 on disks or drums
• CPC Class Codes: H04N 5/782 on tape
• CPC Class Codes: H04N 5/85 on discs or drums
• CPC Class Codes: H04N 5/907 using static stores, e.g. storage tubes or semiconductor memories H04N5/91 takes precedence
• CPC Class Codes: H04N 7/163 by receiver means only
• CPC Class Codes: H04N 9/87 Regeneration of colour television signals H04N9/80 takes precedence
• CPC Class Codes: Y10S 358/908 Pause control, i.e. "commercial killers"

Abstract

Identification of starting and ending times of commercial breaks and commercials within those commercial breaks found in audiovisual content is disclosed. A solution to a “batch optimization” problem is used in which commercial locations within a set of audiovisual content are detected as a group by choosing a set of commercial locations which optimizes a cost function which can include considerations of, for example, 1) one or more cues, 2) relative locations of commercials within the audiovisual content, and/or 3) probability models based on statistics obtained regarding characteristics of typical commercial and commercial breaks. Optimization can be done over the total set of commercial location decisions, rather than on a per-commercial basis. Additionally, the cost function can be iteratively evaluated and many more types of cues and combinations of cues can be used in detection of commercials.

Claims

1. A method for detecting at least one commercial break in a set of audiovisual content spanning a duration of time, each commercial break including at least one commercial, the method comprising:

selecting multiple times within the duration of time spanned by the set of audiovisual content as a current set of commercial boundary times;

selecting a revised set of commercial boundary times including the current set of commercial boundary times and one or more additional times within the duration of time spanned by the set of audiovisual content;

comparing, based on probabilities, the revised set of commercial boundary times to the current set of commercial boundary times to determine whether the revised set of commercial boundary times constitute a better set of commercial boundary times than the current set of commercial boundary times; and

,

identifying the revised set of commercial boundary times as the current set of commercial boundary times, and, again

selecting a revised set of commercial boundary times, and

comparing the revised set of commercial boundary times to the current set of commercial boundary times.

2. The method as in claim 1, wherein selecting a current set of commercial boundary times further comprises:

identifying candidate times within the duration of time spanned by the set of audiovisual content, each candidate time representing a possible boundary time of a commercial; and

selecting candidate times as one of the current set of commercial boundary times based on an evaluation of each candidate time to determine a likelihood that the candidate time is a commercial boundary time.

3. A method for detecting a commercial in a set of audiovisual content spanning a duration of time, the method comprising:

identifying a candidate time within the duration of time spanned by the set of audiovisual content, wherein the candidate time represents a possible boundary time of a commercial; and

evaluating the candidate time to determine a likelihood that the candidate time is a commercial boundary time, wherein the evaluation is based at least in part on one or more characteristics of audiovisual content occurring after the candidate time and associated probabilities.

4. The method as in claim 3, wherein the evaluation is further based, in part, on one or more characteristics of audiovisual content occurring before the candidate time.

5. The method as in claim 3, wherein the evaluation is based on one or more characteristics of audiovisual content occurring throughout the entire duration of time.

6. A method for detecting a commercial in a set of audiovisual content spanning a duration of time, the method comprising:

identifying a candidate time within the duration of time spanned by the set of audiovisual content, the candidate time representing a possible boundary time of a commercial; and

evaluating the candidate time to determine a likelihood that the candidate time is a commercial boundary time, wherein the evaluation is based at least in part on a relationship of the candidate time to one or more other candidate times.

7. The method as in claim 6, wherein the evaluation is based on a relationship of the candidate time to all other candidate times.

8. A non-transitory computer-readable medium storing computer-readable instructions to perform a method for detecting a commercial in a set of audiovisual content spanning a duration of time, the method comprising:

evaluating the audiovisual content to identify the presence of a cue regarding an absence of a usually present network icon; and

identifying a candidate time within the duration of time spanned by the set of audiovisual content based on an evaluation of the identified cue, the candidate time representing a possible boundary time of a commercial.

9. The computer-readable medium as in claim 8, further comprising evaluating the audiovisual content to identify the presence of a cue regarding a sequence of black frames in the visual content, wherein a candidate time is identified based on an evaluation of one or more network icon and black frame cues.

10. The computer-readable medium as in claim 8, further comprising evaluating the audiovisual content to identify the presence of a cue regarding an audio pause in the audio content, wherein a candidate time is identified based on an evaluation of one or more network icon and audio pause cues.

11. The computer-readable medium as in claim 8, further comprising evaluating the audiovisual content to identify the presence of a cue regarding a scene cut or fade in the visual content, wherein a candidate time is identified based on an evaluation of one or more network icon and scene cut/fade cues.

12. The computer-readable medium as in claim 8, further comprising evaluating the audiovisual content to identify the presence of a cue regarding the occurrence of specified closed-captioning formatting signals or the absence of closed-captioning, wherein a candidate time is identified based on an evaluation of one or more network icon and closed-captioning cues.

13. A method for detecting a commercial in a set of audiovisual content spanning a duration of time, the method comprising:

evaluating the audiovisual content to identify the presence of a cue regarding a presence of music in the audio content; and

identifying a candidate time within the duration of time spanned by the set of audiovisual content based on an evaluation of the identified cue, the candidate time representing a possible boundary of a commercial.

14. The method as in claim 13, further comprising evaluating the audiovisual content to identify the presence of a cue regarding a sequence of black frames in the visual content, wherein a candidate time is identified based on an evaluation of one or more music and black frame cues.

15. The method as in claim 13, further comprising evaluating the audiovisual content to identify the presence of a cue regarding an audio pause in the audio content, wherein a candidate time is identified based on an evaluation of one or more music and audio pause cues.

16. The method as in claim 13, further comprising evaluating the audiovisual content to identify the presence of a cue regarding a scene cut or fade in the visual content, wherein a candidate time is identified based on an evaluation of one or more music and scene cut/fade cues.

17. The method as in claim 13, further comprising evaluating the audiovisual content to identify the presence of a cue regarding the occurrence of specified closed-captioning formatting signals or the absence of closed-captioning, wherein a candidate time is identified based on an evaluation of one or more music and closed-captioning cues.

18. The method as in claim 13, further comprising evaluating the audiovisual content to identify the presence of a cue regarding the absence of a usually present network icon, wherein a candidate time is identified based on an evaluation of one or more music and network icon cues.

19. A method for detecting a commercial in a set of audiovisual content spanning a duration of time, the method comprising:

evaluating the audiovisual content to identify the presence of a cue regarding a density of scene cuts or fades in the visual content; and

identifying a candidate time within the duration of time spanned by the set of audiovisual content based on an evaluation of the identified cue, the candidate time representing a possible boundary time of a commercial.

20. The method as in claim 19, further comprising evaluating the audiovisual content to identify the presence of a cue regarding a sequence of black frames in the visual content, wherein a candidate time is identified based on an evaluation of one or more scene cut/fade density and black frame cues.

21. The method as in claim 19, further comprising evaluating the audiovisual content to identify the presence of a cue regarding an audio pause in the audio content, wherein a candidate time is identified based on an evaluation of one or more scene cut/fade density and audio pause cues.

22. The method as in claim 19, further comprising evaluating the audiovisual content to identify the presence of a cue regarding a scene cut or fade in the visual content, wherein a candidate time is identified based on an evaluation of one or more scene cut/fade density and scene cut/fade cues.

23. The method as in claim 19, further comprising evaluating the audiovisual content to identify the presence of a cue regarding the occurrence of specified closed-captioning formatting signals or the absence of closed-captioning, wherein a candidate time is identified based on an evaluation of one or more scene cut/fade density and closed-captioning cues.