Monitoring activity using video information

• US 20060018516A1
• Filed: 07/22/2005
• Published: 01/26/2006
• Est. Priority Date: 07/22/2004
• Status: Abandoned Application

• Current Assignee: Regents of The University of Minnesota (University of Minnesota)
• Original Assignee: Regents of The University of Minnesota (University of Minnesota)
• Inventors: Papanikolopoulos, Nikolaos, Masoud, Osama T., Bird, Nathaniel D.
• Application Number:
  US11/188,288
• Publication Number:
  US 20060018516A1
• Time in Patent Office: Days
• US Class Current: 382/115
• CPC Class Codes: G06T 7/254 involving subtraction of images
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G08B 13/1961 Movement detection not involving frame subtraction, e.g. motion detection on the basis of luminance changes in the image

Abstract

Apparatus and methods for monitoring activity use video information to track activity of a target at a given location. In an embodiment, the target is segmented into portions and a value of a biometric attribute is associated with the target and compared against values of a biometric attributes of corresponding portions of other images to identify the target and determine a length of time that the target is at the given location.

Claims

1. A method comprising:

segmenting an image of a target into a plurality of portions;

determining a value of a biometric attribute for each of the segmented portions; and

comparing each value of the biometric attribute with other values of the biometric attribute of corresponding portions of other images to determine if the image correlates to one or more of the other images.

2. The method of claim 1, wherein segmenting an image of a target includes segmenting an image of an individual.

3. The method of claim 2, wherein segmenting an image of an individual includes segmenting the image into three portions.

4. The method of claim 3, wherein segmenting the image into three portions includes segmenting the image corresponding to a head, a torso, and legs.

5. The method of claim 1, wherein determining a value of a biometric attribute includes determining a value of a short-term biometric attribute.

6. The method of claim 5, wherein determining a value of a short-term biometric attribute includes determining a color.

7. The method of claim 5, wherein determining a value of a short-term biometric attribute includes determining a median color.

8. The method of claim 7, wherein the method includes identifying an individual comparing each median color of the segmented portions with other median colors of corresponding portions of other images and determining a length of time that the identified individual has been at a location.

9. The method of claim 7, wherein the method includes obtaining images of targets at a location;

subtracting background from the images; and

tracking one or more of the targets at the location for which the comparison of median colors is performed to identify the tracked targets.

10. The method of claim 9, obtaining images of targets includes obtaining images of individuals.

11. The method of claim 9, obtaining images of targets includes obtaining images from a single camera.

12. The method of claim 1, wherein the method further includes

receiving a number of action images of an action of the target, each action image being associated with a different time;

constructing feature images from the number of action images of the action;

projecting the feature images in terms of eigenvectors, the eigenvectors formed from a training process;

generating a manifold of the action from the feature images projected in terms of eigenvectors;

comparing the manifold with reference manifolds to classify the action as one of a set of action categories.

13. The method of claim 12, wherein projecting the feature images in terms of eigenvectors includes projecting the feature images in terms of eigenvectors using principle component analysis.

14. The method of claim 12, wherein the method includes performing a training process to determine the eigenvectors from actions in the set of action categories.

15. The method of claim 14, wherein the method includes storing the eigenvectors.

16. The method of claim 12, wherein constructing feature images includes using an infinite impulse response (IIR) filter.

17. The method of claim 16, wherein using an infinite impulse response (IIR) filter includes using responses from the filter as a measure of motion of the action images.

18. The method of claim 12, wherein receiving a number of action images of an action includes receiving each action image of the action performed parallel to a plane of each image.

19. The method of claim 12, wherein comparing the manifold of feature images with reference manifolds includes using a distance measure to define a classifier of the action.

20. The method of claim 12, wherein the method includes providing information to a monitoring control system identifying the action as one of a set of action categories based on comparing the manifold of feature images with reference manifolds.

21. A computer-readable medium having computer-executable instructions for performing a method comprising:

segmenting an image of a target into a plurality of portions;

determining a value of a biometric attribute for each of the segmented portions; and

comparing each value of the biometric attribute with other values of the biometric attribute of corresponding portions of other images to determine if the image correlates to one or more of the other images.

22. The computer-readable medium of claim 21, wherein segmenting an image of a target includes segmenting an image of an individual.

23. The computer-readable medium of claim 21, wherein segmenting the image into three portions includes segmenting the image corresponding to a head, a torso, and legs.

24. The computer-readable medium of claim 21, wherein determining a value of biometric attribute includes determining a value of a short-term biometric attribute.

25. The computer-readable medium of claim 21, wherein determining a value of a short-term biometric attribute includes determining a median color.

26. The computer-readable medium of claim 25, wherein the computer-readable medium includes instructions to identify an individual by comparing each median color of the segmented portions with other median colors of corresponding portions of other images and determining a length of time that the identified individual has been at a location.

27. The computer-readable medium of claim 25, wherein the computer-readable medium includes instructions to:

obtain images of targets at a location;

subtract background from the images; and

track one or more of the targets at the location for which the comparison of median colors is performed to identify the tracked targets.

28. The computer-readable medium of claim 27, wherein to obtain images of targets includes obtaining images of individuals.

29. The computer-readable medium of claim 27, to obtain images of targets includes obtaining images from a single camera.

30. The computer-readable medium of claim 21, wherein the computer-readable medium includes instructions to:

construct feature images from a number of received action images of an action of the target, each action image being associated with a different time the number of action images of the action;

project the feature images in terms of eigenvectors, the eigenvectors formed from a training process;

generate a manifold of the action from the feature images projected in terms of eigenvectors;

compare the manifold with reference manifolds to classify the action as one of a set of action categories.

31. The computer-readable medium of claim 30, wherein to project the feature images in terms of eigenvectors includes projecting the feature images in terms of eigenvectors using principle component analysis.

32. The computer-readable medium of claim 30, wherein the computer-readable medium includes instructions to perform a training process to determine the eigenvectors from actions in the set of action categories.

33. An apparatus comprising:

a video input to receive an image of a target;

an analyzing unit to determine if the image correlates to one or more of other images, the analyzing unit adapted to;

segment the image into a plurality of portions;

determine a value of a biometric attribute for each of the segmented portions; and

compare each value of the biometric attribute with other values of the biometric attribute of corresponding portions of other images.

34. The apparatus of claim 33, wherein the image includes an image of an individual.

35. The apparatus of claim 33, wherein the biometric attribute includes a short-term biometric attribute.

36. The apparatus of claim 35, wherein the short-term biometric attribute includes a color.

37. The apparatus of claim 35, wherein the short-term biometric attribute includes a median color.

38. The apparatus of claim 33, wherein the video input is adapted to receive the image from a camera.

39. A system comprising:

a camera; and

an analyzing unit to receive an image from the camera, the analyzing unit to determine if the image correlates to one or more of other images, the analyzing unit adapted to;

segment an image of a target into a plurality of portions;

determine a value of a biometric attribute for each of the segmented portions; and

compare each value of the biometric attribute with other values of the biometric attribute of corresponding portions of other images.

40. The system of claim 39, wherein the analyzing unit includes a processor coupled to a memory.

41. The system of claim 39, wherein the image includes an image of an individual.

42. The system of claim 39, wherein the biometric attribute includes a short-term biometric attribute.

43. The system of claim 42, wherein the short-term biometric attribute includes a median color.

44. The system of claim 39, wherein the system includes an alarm responsive to the analyzing unit.

45. The system of claim 39, wherein the system includes a memory to store the other values of the biometric attribute.

46. The system of claim 39, wherein the analyzing unit is adapted to:

construct feature images from a number of received action images of an action of the target, each action image being associated with a different time;

project the feature images in terms of eigenvectors, the eigenvectors formed from a training process;

generate a manifold of the action from the feature images projected in terms of eigenvectors;

compare the manifold with reference manifolds to classify the action as one of a set of action categories.

47. The system of claim 45, wherein to project the feature images in terms of eigenvectors includes projecting the feature images in terms of eigenvectors using principle component analysis.

48. The system of claim 46, wherein the analyzing unit is adapted to perform a training process to determine the eigenvectors from actions in the set of action categories.