Determining image quality for improving object trackability

• US 7,986,347 B2
• Filed: 08/13/2009
• Issued: 07/26/2011
• Est. Priority Date: 03/30/2001
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Sun, Wei, Martins, Fernando
• Primary Examiner(s): SELBY, GEVELL V
• Application Number:
  US12/541,033
• Publication Number:
  US 20100007764A1
• Time in Patent Office: 712 Days
• Field of Search: 348/161, 348169-172, 348/207.1, 348/222.1, 348/223.1, 348/152, 382/103
• US Class Current: 348/227.1
• CPC Class Codes: G01S 3/7864 T.V. type tracking systems
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51

Abstract

A method and system are provided. In one embodiment, a video image is preprocessed from a video camera to separate objects to be tracked from the rest of the video image. Statistics for the tracked objects and the rest of the video image are computed. Further, a quality measure based on the statistics for the tracked objects and the rest of the video image are generated. The quality measure indicates the suitability of the video image for use by an object tracking system. The generating of the quality measure maximizes saturation of each of the tracked objects. Further, the video camera is tuned to increase the quality measure beyond a threshold.

Claims

1. A method comprising:

computing statistics for one or more objects of a video image and the rest of the video image;

preprocessing the video image from a video camera to separate the one or more objects to be tracked from the rest of the video image;

generating a quality measure based on the statistics for the one or more objects to be tracked and the rest of the video image that indicates the suitability of the video image for use by an object tracking system, wherein the generating of the quality measure maximizes saturation of each of the one or more objects to be tracked; and

providing the quality measure to the object tracking system.

2. The method of claim 1, further comprising:

and

tuning the video camera to increase the quality measure beyond a threshold.

3. The method of claim 1, wherein the generating of the quality measure comprises:

minimizing saturation of the background of the video image;

maximizing a hue difference between the one or more objects to be tracked; and

maximizing an average hue difference between the one or more objects to be tracked and the background of the video image.

4. The method of claim 1, wherein the preprocessing of the video image from the video camera comprises:

computing color statistics for the one or more objects to be tracked;

removing the one or more objects to be tracked from the background of the video image; and

computing color statistics for the background of the video image.

5. The method of claim 4, wherein the computing of the color statistics for the one or more objects to be tracked comprises:

identifying the one or more objects to be tracked; and

calculating mean and variance values for the hue and saturation of the one or more objects to be tracked.

6. The method of claim 5, wherein the identifying of the one or more objects to be tracked

comprises aligning the one or more objects to be tracked with one or more rectangles projected onto the video image.

7. The method of claim 4, wherein the removing of the one or more objects to be tracked from the background of the video image comprises:

selecting an object of the one or more objects to be tracked;

selecting a pixel within the selected object;

identifying the selected pixel as a pixel belonging to the selected object if the difference between hue of the pixel and mean hue of the selected object is within an allowable range for hue, the difference between saturation of the pixel and mean saturation of the selected object is within an allowable range for saturation, and the horizontal and vertical distances of the pixel from the center of the selected object are within an allowable range for distance; and

identifying the selected pixel as a pixel belonging to the background of the video image if the difference between hue of the pixel and the mean hue of the selected object is not within the allowable range for hue, the difference between saturation of the pixel and the mean saturation of the object selected is not within the allowable range for saturation, and the horizontal and vertical distances of the pixel from the center of the selected object are not within the allowable range for distance.

8. The method of claim 7, wherein the allowable range for hue is ten times the square root of hue variance.

9. The method of claim 7, wherein the allowable range for saturation is ten times the square root of saturation variance.

10. The method of claim 7, wherein the allowable range for distance is ten percent of one of maximum width, and height.

11. The method of claim 4, wherein the computing color statistics for the background of the video image comprises:

calculating a mean value for hue of the background; and

calculating a mean value for saturation of the background.

12. The method as recited in claim 1, wherein at least one of the one or more objects to be tracked comprises a person.

13. The method as recited in claim 12, wherein the person is separable from background images.

14. The method as recited in claim 13, further comprising:

separating the person from the background image comprising;

minimizing saturation of the background of the video image;

maximizing a hue difference between the person to be tracked; and

maximizing an average hue difference between the person to be tracked and the background of the video image.

15. The method as recited in claim 1, wherein the computing statistics further comprises preprocessing a video image from a video camera to separate one or more objects to be tracked from the rest of the video image.

16. The method as recited in claim 15, wherein the at least one of the one or more objects to be tracked comprises a person.

17. A system comprising:

a storage device; and

a processor coupled to the storage device, the processor to compute statistics for one or more objects of a video image and the rest of the video image, and

generate a quality measure based on the statistics for the one or more objects to be tracked and the rest of the video image that indicates the suitability of the video image for use by an object tracking system, wherein the generating of the quality measure maximizes saturation of each of the one or more objects to be tracked.

18. The system of claim 17, wherein the processor is further to:

preprocess the video image from a video camera to separate the one or more objects to be tracked from the rest of the video image; and

tune the video camera to increase the quality measure beyond a threshold.

19. The system of claim 17, wherein the generating of the quality measure comprises:

minimizing saturation of the background of the video image;

maximizing a hue difference between the one or more objects to be tracked; and

maximizing an average hue difference between the one or more objects to be tracked and the background of the video image.

20. The system of claim 18, wherein the preprocessing of the video image from the video camera comprises:

computing color statistics for the one or more objects to be tracked;

removing the one or more objects to be tracked from the background of the video image; and

computing color statistics for the background of the video image.

21. The system of claim 20, wherein the computing color statistics for the background of the video image comprises:

calculating a mean value for hue of the background; and

calculating a mean value for saturation of the background.

22. The system as recited in claim 17, wherein the processor is configured to track a person.

23. The system as recited in claim 22, wherein the person is separable from background images.

24. The system as recited in claim 23, wherein the processor is further configured to:

separate the person from the background image comprising;

minimize saturation of the background of the video image;

maximize a hue difference between the person to be tracked; and

maximize an average hue difference between the person to be tracked and the background of the video image.

25. A non-transitory machine-readable storage medium comprising instructions that when executed by a machine, cause the machine to:

compute statistics for one or more objects of a video image and the rest of the video image;

preprocess the video image from a video camera to separate the one or more objects to be tracked from the rest of the video image;

generate a quality measure based on the statistics for the one or more objects to be tracked and the rest of the video image that indicates the suitability of the video image for use by an object tracking system, wherein the generating of the quality measure maximizes saturation of each of the one or more objects to be tracked; and

provide the quality measure to the object tracking system.

26. The machine-readable storage medium of claim 25, wherein the instructions that when executed, further cause the machine to:

tune the video camera to increase the quality measure beyond a threshold.

27. The machine-readable storage medium of claim 25, wherein the generating the quality measure comprises:

minimizing saturation of the background of the video image;

maximizing a hue difference between the one or more objects to be tracked; and

maximizing an average hue difference between the one or more objects to be tracked and the background of the video image.

28. The machine-readable storage medium of claim 25, wherein the preprocessing of the video image from the video camera comprises:

computing color statistics for the one or more objects to be tracked;

removing the one or more objects to be tracked from the background of the video image; and

computing color statistics for the background of the video image.

29. The medium as recited in claim 25, wherein at least one of the one or more objects to be tracked comprises a person.

30. The medium as recited in claim 29, wherein the person is separable from background images.

31. The medium as recited in claim 30, further comprising

instructions to:

separate the person from the background image comprising instructions to;

minimize saturation of the background of the video image;

maximize a hue difference between the person to be tracked; and

maximize an average hue difference between the person to be tracked and the background of the video image.