AUTOMATIC MEASUREMENT OF ADVERTISING EFFECTIVENESS

• US 20080219504A1
• Filed: 03/04/2008
• Published: 09/11/2008
• Est. Priority Date: 03/05/2007
• Status: Abandoned Application

• Current Assignee: Sportvision, Inc. (Sportsmedia Technology Corporation)
• Original Assignee: Sportvision, Inc. (Sportsmedia Technology Corporation)
• Inventors: White, Marvin S., Pendleton, Rand, Cavallaro, Richard H., Adams, Henry W.
• Application Number:
  US12/041,918
• Publication Number:
  US 20080219504A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06T 7/00 Image analysis

Abstract

An automated system for measuring information about a target image in a video is described. One embodiment includes receiving a set of one or more video images for the video, automatically finding the target image in at least a subset of the video images, determining one or more statistics regarding the target image being in the video, and reporting the one or more statistics.

Claims

1. A machine implemented method for measuring information about a target image in a video, comprising:

receiving a set of video images for the video;

automatically finding the target image in at least a subset of the video images;

determining one or more statistics regarding the target image being in the video; and

reporting about the one or more statistics.

2. A method according to claim 1, wherein:

the determining one or more statistics includes determining total time the target image is in the video.

3. A method according to claim 1, wherein:

the determining one or more statistics includes determining time the target image is in the video during a predefined portion of an event depicted in the video.

4. A method according to claim 1, wherein:

the determining one or more statistics includes determining a percentage of the target image that is visible in the video.

5. A method according to claim 1, wherein:

the determining one or more statistics includes determining a percentage of the video that is filled by the target image.

6. A method according to claim 1, wherein:

the determining one or more statistics includes determining contrast information for the target image.

7. A method according to claim 1, wherein the automatically finding the target image comprises:

accessing data about one or more positions of the target image in one or more previous video images; and

performing image recognition in the subset of video images to find the target image and using the data about the one or more positions of the target image in one or more previous video images to limit the image recognition.

8. A method according to claim 1, wherein the automatically finding the target image comprises:

accessing data about one or more positions of the target image in one or more previous video images;

predicting a location in a current video image based on the one or more positions of the target image in the one or more previous video images;

searching for the target image in a neighborhood of the predicted location in the current video image.

9. A method according to claim 1, wherein the automatically finding the target image comprises:

accessing data about features of the target image, the data about the features is invariant to image scale and rotation; and

searching for and recognizing the features using the data about the features.

10. A method according to claim 1, wherein:

the automatically finding the target image is at least partially based on recognizing the target image in the subset of the set of video images; and

the automatically finding the target image is at least partially based on using camera sensor data.

11. A method according to claim 1, wherein:

the video is of an event; and

the automatically finding the target image includes;

accessing an indication of a boundary at the event,

accessing camera orientation data for a particular video image of the subset of video images,

determining a position of the boundary in the particular video image using the camera orientation data, and

searching for the target image in the particular video image, including using the position of the boundary to restrict the searching.

12. A method according to claim 1, wherein:

the video is of an event;

the target image corresponds to a real world location at the event;

the automatically finding the target image includes;

accessing camera orientation data for a particular video image of the subset of video images,

determining a position in the particular video image of the real world location using the camera orientation data, and

searching for the target image in the particular video image, including using the position in the particular video image of the real world location to restrict the searching.

13. A method according to claim 12, wherein:

the camera orientation data includes camera sensor data.

14. A method according to claim 1, wherein:

the determining includes calculating time of exposure of the target image in the video; and

the reporting includes adjusting exposure time based on what is occurring in the video.

15. A method according to claim 1, wherein:

the determining includes calculating time of exposure of the target image in the video;

the method includes determining rate of movement of the camera; and

the reporting includes adjusting exposure time based on the determined rate of movement of the camera.

16. A machine implemented method for measuring information about a target image in a video, comprising:

receiving a video image from the video;

automatically finding the target image in the video image;

determining one or more statistics regarding the target image being in the video image; and

reporting about the one or more statistics.

17. A method according to claim 16, further comprising:

determining cumulative time the target image is in the video.

18. A method according to claim 16, wherein:

the determining one or more statistics includes determining a percentage of the video that is filled by the target image.

19. One or more processor readable storage devices having processor readable code stored on the one or more processor readable storage devices, the processor readable code programs one or more processors to perform a method comprising:

receiving a particular video image from a video of an event;

automatically finding the target image in the particular video image;

determining one or more statistics regarding the target image being in the particular video image; and

reporting about the one or more statistics.

20. One or more processor readable storage devices according to claim 19, wherein the automatically finding the target image includes:

accessing data about one or more positions of the target image in one or more previous video images; and

searching for the target image in the particular video image, including using the data about one or more positions of the target image in one or more previous video images to restrict the searching.

21. One or more processor readable storage devices according to claim 19, wherein:

the automatically finding the target image is at least partially based on recognizing the target image in the particular video image; and

the automatically finding the target image is at least partially based on using camera sensor data to find the target image in the particular video image.

22. One or more processor readable storage devices according to claim 19, wherein the automatically finding the target image includes:

accessing data about one or more positions of the target image in one or more previous video images;

predicting a location in the particular video image based on the one or more positions of the target image in the one or more previous video images;

searching for the target image in a neighborhood of the predicted location in the particular video image.

23. One or more processor readable storage devices according to claim 19, wherein the automatically finding the target image includes:

accessing data about features of the target image, the data about the features is invariant to image scale and rotation; and

searching for and recognizing the features using the data about the features.

24. One or more processor readable storage devices according to claim 19, wherein the automatically finding the target image includes:

accessing an indication of a boundary at the event;

accessing camera orientation data for the particular video image;

determining a position of the boundary in the particular video image using the camera orientation data; and

searching for the target image in the particular video image, including using the position of the boundary to restrict the searching.

25. One or more processor readable storage devices according to claim 19, wherein:

the target image corresponds to a real world location at the event; and

the automatically finding the target image includes;

accessing camera orientation data for the particular video image,

determining a position in the particular video image of the real world location using the camera orientation data, and

searching for the target image in the particular video image, including using the position in the particular video image of the real world location to restrict the searching.

26. An apparatus that measures information about a target image in a video, comprising:

a communication interface, the communication interface receives the video;

a storage device, the storage device stores the received video; and

a processor in communication with the storage device and the communication interface, the processor finds the target image in the video and determines statistics about the target image being in the video.

27. An apparatus according to claim 26, wherein:

the processor accesses data about one or more positions of the target image in one or more previous video images and searches for the target image in a current video image using the data about one or more positions of the target image in the one or more previous video images to restrict the searching.

28. An apparatus according to claim 26, wherein:

the processor finds the target image based on recognizing the target image in a particular video image and based on using camera sensor data.

29. An apparatus according to claim 26, wherein:

the processor accesses data about one or more positions of the target image in one or more previous video images and predicts a location in a current video image based on the one or more positions of the target image in the one or more previous video images; and

the processor searches for the target image in a neighborhood of the predicted location in the current video image.

30. An apparatus according to claim 26, wherein:

the processor accesses data about features of the target image, the data about the features is invariant to image scale and rotation; and

the processor searches for and recognizes the features using the data about the features.

31. An apparatus according to claim 26, wherein:

the processor accesses an indication of a boundary at the event;

the processor accesses camera orientation data for a particular video image;

the processor determines a position of the boundary in the particular video image using the camera orientation data; and

the processor searches for the target image in the particular video image, including using the position of the boundary to restrict the searching.

32. An apparatus according to claim 26, wherein:

the target image corresponds to a real world location at the event;

the processor accesses camera orientation data for a particular video image;

the processor determines a position in the particular video image of the real world location using the camera orientation data; and

the processor searches for the target image in the particular video image, including using the position in the particular video image of the real world location to restrict the searching.

33. A machine implemented method for measuring information about target images in a video, comprising:

receiving a set of video images for the video;

automatically finding the target images in at least a subset of the video images;

determining separate sets of statistics for each target relating to the respective target image being in the video; and

reporting about the sets of statistics.