Determining image quality for improving object trackability

• US 20060023072A1
• Filed: 09/30/2005
• Published: 02/02/2006
• Est. Priority Date: 03/30/2001
• Status: Active Grant

• Current Assignee: Fernando Martins, Wei Sun
• Original Assignee: Fernando Martins, Wei Sun
• Inventors: Sun, Wei, Martins, Fernando
• Granted Patent: US 7,595,820 B2
• Time in Patent Office: Days
• US Class Current: 348/207.990
• 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:

preprocessing a video image from a video camera to separate one or more objects to be tracked from the rest of the video image and compute statistics for the one or more objects to be tracked and 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

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

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

minimizing the saturation of the background of the video image;

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

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

3. The method of claim 1, wherein the preprocessing 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.

4. The method of claim 3, wherein the computing of color statistics for the one or more objects to be tracked comprises:

identifying the objects to be tracked; and

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

5. The method of claim 4, wherein the identifying comprises aligning the at least one or more objects to be tracked with one or more rectangles projected onto the video image.

6. The method of claim 3, wherein the removing the one or more objects to be tracked from the background of the video image comprises:

selecting one 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 a pixel as 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.

7. The method of claim 6, wherein the allowable range for hue is ten times the square root of hue variance.

8. The method of claim 6, wherein the allowable range for saturation is ten times the square root of saturation variance.

9. The method of claim 6, wherein the allowable range for distance is ten percent of one of maximum width, and height.

10. The method of claim 3, 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.

11. A system comprising:

a storage device; and

a processor coupled to the storage device, the processor to

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

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

12. The system of claim 11, wherein the generating of the quality measure comprises:

minimizing the saturation of the background of the video image;

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

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

13. The system of claim 11, wherein the preprocessing 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.

14. The system of claim 13, 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.

15. A machine-readable medium having stored thereon data representing sets of instructions, the sets of instructions which, when executed by a machine, cause the machine to:

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

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

16. The machine-readable medium of claim 15, wherein the generating the quality measure comprises:

minimizing the saturation of the background of the video image;

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

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

17. The machine-readable medium of claim 15, wherein the preprocessing 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.

18. The machine-readable medium of claim 17, wherein the computing color statistics for the one or more objects to be tracked comprises:

identifying the objects to be tracked; and

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

19. The machine-readable medium of claim 17, wherein the removing the one or more objects to be tracked from the background of the video image comprises:

selecting one 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 a pixel as 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.

20. The machine-readable medium of claim 17, 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.