Object recognition system and process for identifying people and objects in an image of a scene

• US 20030215134A1
• Filed: 06/18/2003
• Published: 11/20/2003
• Est. Priority Date: 11/23/1999
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: John Krumm
• Inventors: Krumm, John
• Granted Patent: US 6,952,496 B2
• Time in Patent Office: Days
• US Class Current: 382/170
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06V 10/56 relating to colour
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation

Abstract

An object recognition system and process that identifies people and objects depicted in an image of a scene. In general, this system and process entails first creating model histograms of the people and objects that it is desired to identify in the image. Then, the image is segmented to extract regions which likely correspond to the people and objects being identified. A histogram is computed for each of the extracted regions, and the degree of similarity between each extracted region histogram and each of the model histograms is assessed. The extracted regions having a histogram that exhibits a degree of similarity to one of the model histograms which exceeds a prescribed threshold is designated as corresponding to the person or object associated with that model histogram. Finally, the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram can be stored as an additional model histogram associated with that person or object. Preferably, the foregoing general system and process is repeated for subsequently generated images of the scene, so that the identity of people and objects can be monitored over time as they move into and about the scene. In addition, preferably color images of the scene and color histograms are employed in the object recognition system and process.

Claims

1. A computer-implemented process for identifying a person or object in an image of a scene, said process comprising using a computer to perform the following process actions:

creating model histograms of people and objects that it is desired to identify in said image of the scene;

segmenting said image to extract regions which correspond to at least one person or object whose identity it is desired to determine;

for each region extracted from the image,

computing a histogram for the extracted region, and

respectively producing an indicator of the degree of similarity between the extracted region histogram and each of said model histograms;

forming exclusive combinations of said degree of similarity indicators wherein each combination is made up of one indicator associated with each extracted region of the image and each indicator in the combination is derived from a different model histogram;

computing a combined degree of similarity value for each of said indicator combinations;

identifying the largest combined degree of similarity value; and

designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which exceeds a prescribed threshold as corresponding to the person or object associated with the model histogram used in part to compute the individual one of the indicators.

2. The process of claim 1, further comprising the process action of repeating said segmenting, first computing, producing, forming, second computing, identifying and designating actions for successive images of the scene so as to track the identity of persons and objects in the scene over time.

3. The process of claim 1, wherein the image is a color image of the scene, and the model histograms and histograms computed for each of region extracted from the color image are color histograms.

4. The process of claim 3, wherein the process action of creating model histograms of people and objects that it is desired to identify in said image of the scene, comprises the actions of:

capturing one or more model images of the people and objects that it is desired to identify;

segmenting said model image to extract model regions which correspond to each of said people and objects whose identity it is desired to determine; and

for each model region,

determining the actual colors exhibited by the pixels of the model region;

dividing the overall gamut of actual colors exhibited by the pixels of the extracted model region into a series of discrete color ranges, hereinafter referred to as quantized color categories;

respectively assigning each pixel of the extracted model region to the quantized color category into which the actual color of the pixel falls, and

establishing a count of the number of pixels of the extracted model region assigned to the same quantized color category.

5. The process of claim 4, wherein the process action of computing a histogram for each of region extracted from the image, comprises the actions of:

determining the actual colors exhibited by the pixels of the extracted region;

dividing the overall gamut of actual colors exhibited by the pixels of the extracted region into a series of discrete color ranges, hereinafter referred to as quantized color categories;

respectively assigning each pixel of the extracted region to the quantized color category into which the actual color of the pixel falls; and

establishing a count of the number of pixels of the extracted region assigned to the same quantized color category.

6. The process of claim 5, wherein the process actions of dividing the overall gamut of actual colors exhibited by the pixels of the extracted model regions and extracted image regions into a series of discrete color ranges, comprises the action of employing the same quantized color categories for each.

7. The process of claim 6, wherein the process action of respectively producing an indicator of the degree of similarity between the extracted region histogram and each of said model histograms, comprises the actions of:

respectively comparing the pixel count from each quantized color category of the histogram derived from the extracted region to the pixel count from the corresponding quantized color category of each model histograms;

identifying the smaller of the two counts in each quantized color category for each pair of histograms compared;

summing the smaller counts from each quantized color category to produce a separate similarity value for each pair of histograms compared; and

normalizing the similarity value for each pair of histograms compared by dividing it by a maximum possible similarity value to produce a match quality indicator.

8. The process of claim 7, wherein the process action of computing a combined degree of similarity value for each of said indicator combinations, comprises the action of summing the match quality indicators in each combination to produce a combined indicator for each combination.

9. The process of claim 1, further comprising the process action of storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object.

10. The process of claim 1, further comprising the process action of designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which does not exceed the prescribed threshold as corresponding to a person or object of unknown identity.

11. A computer-implemented process for identifying a person or object in an image of a scene, said process comprising using a computer to perform the following process actions:

creating model histograms of people and objects that it is desired to identify in said image of the scene;

dividing the image into a plurality of cells;

assigning each model histogram to one of the image cells;

segmenting said image to extract regions which correspond to at least one person or object whose identity it is desired to determine;

for each region extracted from the image,

computing a histogram for the extracted region,

determining the centroid of the extracted region and identifying the cell in which it resides,

for each of a set of one or more model histograms associated with the same person or object, ascertaining the closest image cell to the identified cell, including the identified cell itself, that has a histogram associated with that person or object assigned thereto,

respectively assessing the degree of similarity between the histogram computed for the extracted region and each of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region,

determining whether the extracted region'"'"'s histogram exhibits a degree of similarity to one of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region which exceeds a prescribed threshold, and

whenever the extracted region'"'"'s histogram exhibits a degree of similarity to one of said previously ascertained model histograms which exceeds the prescribed threshold, designating the extracted region as corresponding to the person or object associated with that model histogram.

12. The process of claim 11, wherein the process action of creating model histograms, comprises the actions of:

obtaining at least one prefatory image of the scene which depict the people and objects that it is desired to identify in a subsequent image of the scene;

dividing each prefatory image into a plurality of cells;

segmenting each of the prefatory images to extract regions which correspond to at least one person or object whose identity is known; and

for each region extracted from the prefatory images,

computing a histogram for the extracted region to produce a model histogram associated with the person or object represented by the extracted region, and

determining the centroid of the extracted region and identifying the cell in which it resides; and

wherein

the process action of assigning each model histogram to one of the image cells comprises the action of respectively assigning each model histogram to the cell which corresponds to the cell of the prefatory image where the centroid of the extracted region associated with each model histogram was determined to reside.

13. The process of claim 11, wherein the process action of creating model histograms, comprises the actions of:

obtaining at least one model image which depict the people and objects that it is desired to identify in said image of the scene;

segmenting each of the model images to extract regions which correspond to at least one person or object whose identity is known; and

for each region extracted from the model images, computing a histogram for the extracted region to produce a model histogram associated with the person or object represented by the extracted region.

14. The process of claim 11, further comprising the process action of designating the selected region as corresponding to a person or object of unknown identity whenever the selected region'"'"'s histogram does not exhibit a degree of similarity to any of said previously ascertained model histograms which exceeds the prescribed threshold.

15. The process of claim 11, further comprising the process action of repeating said segmenting, dividing, assigning, segmenting, computing, determining, ascertaining, assessing, second determining, and designating actions for successive images of the scene so as to track the identity of persons and objects in the scene over time.

16. The process of claim 11, wherein the image is a color image of the scene, and the model histograms and histograms computed for each of region extracted from the color image are color histograms.

17. The process of claim 11, further comprising the process action of storing the histogram computed for the selected region of the image that is designated as corresponding to a person or object associated with one of the model histograms as an additional model histogram associated with that person or object and assigning the newly stored histogram to the cell in which the centroid of the corresponding extracted region resides.

18. The process of claim 17, wherein the process action of storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object, comprises the actions of:

for each region extracted from the image and designated as corresponding to a person or object associated with a model histogram,

determining whether a histogram, associated with the person or object corresponding to the histogram derived from the extracted region, was previously stored and assigned to the cell containing the centroid of the extracted region, and

storing the histogram derived from the extracted region as an additional model histogram and assigning the newly stored histogram to the cell containing the centroid of the extracted region whenever it is determined that a histogram associated with the person or object corresponding to the histogram derived from the extracted region was not previously stored and assigned to the cell containing the centroid of the extracted region.

19. The process of claim 17, wherein the process action of storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object, comprises the actions of:

for each region extracted from the image and designated as corresponding to a person or object associated with a model histogram,

determining whether a histogram, associated with the person or object corresponding to the histogram derived from the extracted region, was previously stored and assigned to the cell containing the centroid of the extracted region,

whenever it is determined that a histogram associated with the person or object corresponding to the histogram derived from the extracted region was previously stored and assigned to the cell containing the centroid of the extracted region, identifying the time when said previously stored histogram was stored and assigned,

ascertaining whether the previously stored histogram was stored within a prescribed threshold time frame from the current time, and

storing the histogram derived from the extracted region as an additional model histogram and assigning the newly stored histogram to the cell containing the centroid of the extracted region whenever it is determined that the previously stored histogram was not stored within the prescribed threshold time frame from the current time.

20. An object recognition system for identifying a person or object in an image of a scene, comprising:

a general purpose computing device;

a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to,

(a) create model histograms of people and objects that it is desired to identify in said image of the scene,

(b) segment said image to extract regions which correspond to at least one person or object whose identity it is desired to determine,

(c) compute a histogram for each of region extracted from the image,

(d) respectively producing an indicator of the degree of similarity between each extracted region histogram and each of said model histograms,

(e) forming exclusive combinations of said degree of similarity indicators wherein each combination is made up of one indicator associated with each extracted region of the image and each indicator in the combination is derived from a different model histogram,

(f) computing a combined degree of similarity value for each of said indicator combinations,

(g) identifying the largest combined degree of similarity value, and

(h) designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which exceeds a prescribed threshold as corresponding to the person or object associated with the model histogram used in part to compute the individual one of the indicators.

21. The system of claim 20, further comprising a program module for repeating actions (b) through (h) for successive images of the scene so as to track the identity of persons and objects in the scene over time.

22. The system of claim 20, wherein the image is a color image of the scene, and the model histograms and histograms computed for each of region extracted from the color image are color histograms.

23. The system of claim 22, wherein the program module for creating model histograms of people and objects that it is desired to identify in said image of the scene, comprises sub-modules for:

capturing one or more model images of the people and objects that it is desired to identify;

segmenting said model image to extract model regions which correspond to each of said people and objects whose identity it is desired to determine; and

for each model region,

determining the actual colors exhibited by the pixels of the model region;

dividing the overall gamut of actual colors exhibited by the pixels of the extracted model region into a series of discrete color ranges, hereinafter referred to as quantized color categories;

respectively assigning each pixel of the extracted model region to the quantized color category into which the actual color of the pixel falls, and

establishing a count of the number of pixels of the extracted model region assigned to the same quantized color category.

24. The system of claim 23, wherein the program module for computing a histogram for each of region extracted from the image, comprises sub-modules for:

determining the actual colors exhibited by the pixels of the extracted region;

dividing the overall gamut of actual colors exhibited by the pixels of the extracted region into a series of discrete color ranges, hereinafter referred to as quantized color categories;

respectively assigning each pixel of the extracted region to the quantized color category into which the actual color of the pixel falls; and

establishing a count of the number of pixels of the extracted region assigned to the same quantized color category.

25. The system of claim 24, wherein the sub-modules for dividing the overall gamut of actual colors exhibited by the pixels of the extracted model regions and extracted image regions into a series of discrete color ranges, comprise a sub-module for employing the same quantized color categories for each.

26. The system of claim 25, wherein the program module for respectively producing an indicator of the degree of similarity between the extracted region histogram and each of said model histograms, comprises sub-modules for:

respectively comparing the pixel count from each quantized color category of the histogram derived from the extracted region to the pixel count from the corresponding quantized color category of each model histograms;

identifying the smaller of the two counts in each quantized color category for each pair of histograms compared;

summing the smaller counts from each quantized color category to produce a separate similarity value for each pair of histograms compared; and

normalizing the similarity value for each pair of histograms compared by dividing it by a maximum possible similarity value to produce a match quality indicator.

27. The system of claim 26, wherein the sub-module for computing a combined degree of similarity value for each of said indicator combinations, comprises a sub-module for summing the match quality indicators in each combination to produce a combined indicator for each combination.

28. The system of claim 20, further comprising a program module for storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object.

29. The system of claim 20, further comprising a program module for designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which does not exceed the prescribed threshold as corresponding to a person or object of unknown identity.

30. An object recognition system for identifying a person or object in an image of a scene, comprising:

a general purpose computing device;

a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to,

(a) create model histograms of people and objects that it is desired to identify in said image of the scene,

(b) divide the image into a plurality of cells,

(c) assign each model histogram to one of the image cells,

(d) segment said image to extract regions which correspond to at least one person or object whose identity it is desired to determine,

(e) for each region extracted from the image,

compute a histogram for the extracted region,

determine the centroid of the extracted region and identifying the cell in which it resides,

for each of a set of one or more model histograms associated with the same person or object, ascertain the closest image cell to the identified cell, including the identified cell itself, that has a histogram associated with that person or object assigned thereto,

respectively assess the degree of similarity between the histogram computed for the extracted region and each of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region,

determine whether the extracted region'"'"'s histogram exhibits a degree of similarity to one of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region which exceeds a prescribed threshold, and

whenever the extracted region'"'"'s histogram exhibits a degree of similarity to one of said previously ascertained model histograms which exceeds the prescribed threshold, designate the extracted region as corresponding to the person or object associated with that model histogram.

31. The system of claim 30, wherein the program module for creating model histograms, comprises sub-modules for:

obtaining at least one prefatory image of the scene which depict the people and objects that it is desired to identify in a subsequent image of the scene;

dividing each prefatory image into a plurality of cells;

segmenting each of the prefatory images to extract regions which correspond to at least one person or object whose identity is known; and

for each region extracted from the prefatory images,

computing a histogram for the extracted region to produce a model histogram associated with the person or object represented by the extracted region, and

determining the centroid of the extracted region and identifying the cell in which it resides; and

wherein

the program module for assigning each model histogram to one of the image cells comprises a sub-module for respectively assigning each model histogram to the cell which corresponds to the cell of the prefatory image where the centroid of the extracted region associated with each model histogram was determined to reside.

32. The system of claim 30, wherein the program module for creating model histograms, comprises sub-modules for:

obtaining at least one model image which depict the people and objects that it is desired to identify in said image of the scene;

segmenting each of the model images to extract regions which correspond to at least one person or object whose identity is known; and

for each region extracted from the model images, computing a histogram for the extracted region to produce a model histogram associated with the person or object represented by the extracted region.

33. The system of claim 30, further comprising the program module for designating the selected region as corresponding to a person or object of unknown identity whenever the selected region'"'"'s histogram does not exhibit a degree of similarity to any of said previously ascertained model histograms which exceeds the prescribed threshold.

34. The system of claim 30, further comprising the program module for repeating program modules (b) through (e) for successive images of the scene so as to track the identity of persons and objects in the scene over time.

35. The system of claim 30, wherein the image is a color image of the scene, and the model histograms and histograms computed for each of region extracted from the color image are color histograms.

36. The system of claim 30, further comprising the program module for storing the histogram computed for the selected region of the image that is designated as corresponding to a person or object associated with one of the model histograms as an additional model histogram associated with that person or object and assigning the newly stored histogram to the cell in which the centroid of the corresponding extracted region resides.

37. The system of claim 36, wherein the program module for storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object, comprises sub-modules for:

for each region extracted from the image and designated as corresponding to a person or object associated with a model histogram,

determining whether a histogram, associated with the person or object corresponding to the histogram derived from the extracted region, was previously stored and assigned to the cell containing the centroid of the extracted region, and

storing the histogram derived from the extracted region as an additional model histogram and assigning the newly stored histogram to the cell containing the centroid of the extracted region whenever it is determined that a histogram associated with the person or object corresponding to the histogram derived from the extracted region was not previously stored and assigned to the cell containing the centroid of the extracted region.

38. The system of claim 36, wherein the program module for storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object, comprises sub-modules for:

for each region extracted from the image and designated as corresponding to a person or object associated with a model histogram,

determining whether a histogram, associated with the person or object corresponding to the histogram derived from the extracted region, was previously stored and assigned to the cell containing the centroid of the extracted region,

whenever it is determined that a histogram associated with the person or object corresponding to the histogram derived from the extracted region was previously stored and assigned to the cell containing the centroid of the extracted region, identifying the time when said previously stored histogram was stored and assigned,

ascertaining whether the previously stored histogram was stored within a prescribed threshold time frame from the current time, and

storing the histogram derived from the extracted region as an additional model histogram and assigning the newly stored histogram to the cell containing the centroid of the extracted region whenever it is determined that the previously stored histogram was not stored within the prescribed threshold time frame from the current time.

39. A computer-readable memory for causing a computer to perform an object recognition procedure for identifying a person or object in an image of a scene, comprising:

a general purpose computing device;

a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to,

(a) create model histograms of people and objects that it is desired to identify in said image of the scene,

(b) segment said image to extract regions which correspond to at least one person or object whose identity it is desired to determine,

(c) compute a histogram for each of region extracted from the image,

(d) respectively producing an indicator of the degree of similarity between each extracted region histogram and each of said model histograms,

(e) forming exclusive combinations of said degree of similarity indicators wherein each combination is made up of one indicator associated with each extracted region of the image and each indicator in the combination is derived from a different model histogram,

(f) computing a combined degree of similarity value for each of said indicator combinations,

(g) identifying the largest combined degree of similarity value, and

(h) designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which exceeds a prescribed threshold as corresponding to the person or object associated with the model histogram used in part to compute the individual one of the indicators.

40. The computer-readable memory of claim 39, further comprising a program module for repeating actions (b) through (h) for successive images of the scene so as to track the identity of persons and objects in the scene over time.

41. The computer-readable memory of claim 39, wherein the image is a color image of the scene, and the model histograms and histograms computed for each of region extracted from the color image are color histograms.

42. The computer-readable memory of claim 41, wherein the program module for creating model histograms of people and objects that it is desired to identify in said image of the scene, comprises sub-modules for:

capturing one or more model images of the people and objects that it is desired to identify;

segmenting said model image to extract model regions which correspond to each of said people and objects whose identity it is desired to determine; and

for each model region,

determining the actual colors exhibited by the pixels of the model region;

dividing the overall gamut of actual colors exhibited by the pixels of the extracted model region into a series of discrete color ranges, hereinafter referred to as quantized color categories;

respectively assigning each pixel of the extracted model region to the quantized color category into which the actual color of the pixel falls, and

establishing a count of the number of pixels of the extracted model region assigned to the same quantized color category.

43. The computer-readable memory of claim 42, wherein the program module for computing a histogram for each of region extracted from the image, comprises sub-modules for:

determining the actual colors exhibited by the pixels of the extracted region;

dividing the overall gamut of actual colors exhibited by the pixels of the extracted region into a series of discrete color ranges, hereinafter referred to as quantized color categories;

respectively assigning each pixel of the extracted region to the quantized color category into which the actual color of the pixel falls; and

establishing a count of the number of pixels of the extracted region assigned to the same quantized color category.

44. The computer-readable memory of claim 43, wherein the sub-modules for dividing the overall gamut of actual colors exhibited by the pixels of the extracted model regions and extracted image regions into a series of discrete color ranges, comprise a sub-module for employing the same quantized color categories for each.

45. The computer-readable memory of claim 44, wherein the program module for respectively producing an indicator of the degree of similarity between the extracted region histogram and each of said model histograms, comprises sub-modules for:

respectively comparing the pixel count from each quantized color category of the histogram derived from the extracted region to the pixel count from the corresponding quantized color category of each model histograms;

identifying the smaller of the two counts in each quantized color category for each pair of histograms compared;

summing the smaller counts from each quantized color category to produce a separate similarity value for each pair of histograms compared; and

normalizing the similarity value for each pair of histograms compared by dividing it by a maximum possible similarity value to produce a match quality indicator.

46. The computer-readable memory of claim 45, wherein the sub-module for computing a combined degree of similarity value for each of said indicator combinations, comprises a sub-module for summing the match quality indicators in each combination to produce a combined indicator for each combination.

47. The computer-readable memory of claim 39, further comprising a program module for storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object.

48. The computer-readable memory of claim 39, further comprising a program module for designating each extracted region having a histogram associated with an individual one of the indicators used to compute the identified largest combined degree of similarity value which does not exceed the prescribed threshold as corresponding to a person or object of unknown identity.

49. A computer-readable memory for causing a computer to perform an object recognition procedure for identifying a person or object in an image of a scene, comprising:

a general purpose computing device;

a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to,

(a) create model histograms of people and objects that it is desired to identify in said image of the scene,

(b) divide the image into a plurality of cells,

(c) assign each model histogram to one of the image cells,

(d) segment said image to extract regions which correspond to at least one person or object whose identity it is desired to determine,

(e) for each region extracted from the image,

compute a histogram for the extracted region,

determine the centroid of the extracted region and identifying the cell in which it resides,

for each of a set of one or more model histograms associated with the same person or object, ascertain the closest image cell to the identified cell, including the identified cell itself, that has a histogram associated with that person or object assigned thereto,

respectively assess the degree of similarity between the histogram computed for the extracted region and each of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region,

determine whether the extracted region'"'"'s histogram exhibits a degree of similarity to one of the model histograms previously ascertained to be in a cell closest to the identified cell of the extracted region which exceeds a prescribed threshold, and

whenever the extracted region'"'"'s histogram exhibits a degree of similarity to one of said previously ascertained model histograms which exceeds the prescribed threshold, designate the extracted region as corresponding to the person or object associated with that model histogram.

50. The computer-readable memory of claim 49, wherein the program module for creating model histograms, comprises sub-modules for:

obtaining at least one prefatory image of the scene which depict the people and objects that it is desired to identify in a subsequent image of the scene;

dividing each prefatory image into a plurality of cells;

segmenting each of the prefatory images to extract regions which correspond to at least one person or object whose identity is known; and

for each region extracted from the prefatory images,

computing a histogram for the extracted region to produce a model histogram associated with the person or object represented by the extracted region, and

determining the centroid of the extracted region and identifying the cell in which it resides; and

wherein

the program module for assigning each model histogram to one of the image cells comprises a sub-module for respectively assigning each model histogram to the cell which corresponds to the cell of the prefatory image where the centroid of the extracted region associated with each model histogram was determined to reside.

51. The computer-readable memory of claim 49, wherein the program module for creating model histograms, comprises sub-modules for:

obtaining at least one model image which depict the people and objects that it is desired to identify in said image of the scene;

segmenting each of the model images to extract regions which correspond to at least one person or object whose identity is known; and

for each region extracted from the model images, computing a histogram for the extracted region to produce a model histogram associated with the person or object represented by the extracted region.

52. The computer-readable memory of claim 49, further comprising the program module for designating the selected region as corresponding to a person or object of unknown identity whenever the selected region'"'"'s histogram does not exhibit a degree of similarity to any of said previously ascertained model histograms which exceeds the prescribed threshold.

53. The computer-readable memory of claim 49, further comprising the program module for repeating program modules (b) through (e) for successive images of the scene so as to track the identity of persons and objects in the scene over time.

54. The computer-readable memory of claim 49, wherein the image is a color image of the scene, and the model histograms and histograms computed for each of region extracted from the color image are color histograms.

55. The computer-readable memory of claim 49, further comprising the program module for storing the histogram computed for the selected region of the image that is designated as corresponding to a person or object associated with one of the model histograms as an additional model histogram associated with that person or object and assigning the newly stored histogram to the cell in which the centroid of the corresponding extracted region resides.

56. The computer-readable memory of claim 55, wherein the program module for storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object, comprises sub-modules for:

for each region extracted from the image and designated as corresponding to a person or object associated with a model histogram,

determining whether a histogram, associated with the person or object corresponding to the histogram derived from the extracted region, was previously stored and assigned to the cell containing the centroid of the extracted region, and

storing the histogram derived from the extracted region as an additional model histogram and assigning the newly stored histogram to the cell containing the centroid of the extracted region whenever it is determined that a histogram associated with the person or object corresponding to the histogram derived from the extracted region was not previously stored and assigned to the cell containing the centroid of the extracted region.

57. The computer-readable memory of claim 55, wherein the program module for storing the histogram computed for any extracted region of the image that is designated as corresponding to a person or object associated with a model histogram as an additional model histogram associated with that person or object, comprises sub-modules for:

for each region extracted from the image and designated as corresponding to a person or object associated with a model histogram,

determining whether a histogram, associated with the person or object corresponding to the histogram derived from the extracted region, was previously stored and assigned to the cell containing the centroid of the extracted region,

whenever it is determined that a histogram associated with the person or object corresponding to the histogram derived from the extracted region was previously stored and assigned to the cell containing the centroid of the extracted region, identifying the time when said previously stored histogram was stored and assigned,

ascertaining whether the previously stored histogram was stored within a prescribed threshold time frame from the current time, and

storing the histogram derived from the extracted region as an additional model histogram and assigning the newly stored histogram to the cell containing the centroid of the extracted region whenever it is determined that the previously stored histogram was not stored within the prescribed threshold time frame from the current time.