Image Processing for a Traffic Control System

• US 20090040069A1
• Filed: 08/10/2007
• Published: 02/12/2009
• Est. Priority Date: 08/10/2007
• Status: Active Grant

• Current Assignee: Asian Institute of Technology
• Original Assignee: Asian Institute of Technology
• Inventors: Phuong, Huynh Huu, Afzulpurkar, Nitin
• Granted Patent: US 7,646,311 B2
• Time in Patent Office: Days
• US Class Current: 340/934
• CPC Class Codes: G06V 20/54 of traffic, e.g. cars on the road, trains or boats
• CPC Class Codes: G08G 1/04 using optical or ultrasonic detectors

Abstract

Embodiments of the invention include methods and apparatuses relation to traffic control systems. In one embodiment, a traffic condition is identified by capturing a traffic image, masking the traffic image to obtain an image portion including a traffic image object, and generating the traffic condition based on the traffic image object and the traffic image portion.

Claims

1. A traffic monitoring method comprising:

obtaining an image of a traffic location, the image of the traffic location including an image portion;

generating an image mask, the image mask being dimensionally representative of the image portion;

masking the image of the traffic location using the image mask to obtain the image portion, the image portion including an image object;

identifying the image object from the image portion; and

determining a traffic condition representative of the image portion based at least in part on the identified image object.

2. The traffic monitoring method of claim 1, wherein the traffic condition comprises a traffic density.

3. The traffic monitoring method of claim 2, wherein determining the traffic density comprises:

detecting an edge of the image object;

drawing an image-bounding box around the edge of the image object;

translating the image-bounding box from a perspective coordinate frame to a world coordinate frame;

determining an area of the image-bounding box in the world coordinate frame;

determining an area of the image portion in the world coordinate frame; and

determining the traffic density as a ratio of the area of the image-bounding box in the world coordinate frame to the area of the image portion in the world coordinate frame.

4. The traffic monitoring method of claim 3, further comprising:

enhancing the edge of the image object by applying a morphological function.

5. The traffic monitoring method of claim 2, further comprising:

determining a second traffic density for a second image portion of the image of the traffic location; and

determining a duration for a green traffic light based at least in part upon the traffic density and the second traffic density.

6. The traffic monitoring method of claim 5, further comprising:

comparing the duration for the green traffic light to a maximum green light duration; and

if the duration for the green traffic light is less than the maximum green light duration, then comparing the traffic density to a minimum traffic density; and

if the traffic density is less than the minimum traffic density, then switching the green traffic light off.

7. The traffic monitoring method of claim 6, further comprising:

if the traffic density is greater than the minimum traffic density, then obtaining a second image of the traffic location.

8. The traffic monitoring method of claim 1, wherein obtaining the image of the traffic location comprises obtaining the image of the traffic location from a remote camera.

9. The traffic monitoring method of claim 1, further comprising:

generating control signals for transmission to a traffic station for enabling traffic control.

10. The traffic monitoring method of claim 1, wherein generating the image mask comprises:

identifying a plurality of lines within the traffic image;

identifying a first line and a second line from the plurality of lines, the first line and the second line being the two longest of the plurality of lines; and

defining the image mask between the first line, the second line, and a portion of a boundary of the traffic image.

11. The traffic monitoring method of claim 10, wherein identifying the plurality of lines comprises a Hough transform.

19. The machine readable medium of claim 11, further comprising instructions that, when executed, cause the machine to monitor traffic by:

generating control signals for transmission to a traffic station for enabling traffic control.

20. The machine readable medium of claim 11, wherein generating the image mask comprises:

identifying a plurality of lines within the traffic image;

identifying a first line and a second line from the plurality of lines, the first line and the second line being the two longest of the plurality of lines; and

defining the image mask between the first line and the second line, and a portion of a boundary of the traffic image.

21. The machine readable medium of claim 20, wherein identifying the plurality of lines comprises a Hough transform.

12. A machine readable medium having stored therein a plurality of instructions that, when executed, cause the machine to monitor traffic by:

receiving an image of a traffic location, the image of the traffic location including an image portion;

generating an image mask, the image mask being dimensionally representative of the image portion;

masking the image of the traffic location using the image mask to obtain the image portion, the image portion including an image object;

identifying the image object from the image portion; and

determining a traffic condition representative of the image portion based at least in part on the identified image object.

13. The machine readable medium of claim 12, wherein the traffic condition comprises a traffic density.

14. The machine readable medium of claim 13, wherein determining the traffic density comprises:

detecting an edge of the image object;

generating an image-bounding box around the edge of the image object;

translating the image-bounding box from a perspective coordinate frame to a world coordinate frame;

determining an area of the image-bounding box in the world coordinate frame;

determining an area of the image portion in the world coordinate frame; and

determining the traffic density as a ratio of the area of the image-bounding box in the world coordinate frame to the area of the image portion in the world coordinate frame.

15. The machine readable medium of claim 14, further comprising instructions that, when executed, cause the machine to monitor traffic by:

enhancing the edge of the image object by applying a morphological function.

16. The machine readable medium of claim 13, further comprising instructions that, when executed, cause the machine to monitor traffic by:

determining a second traffic density for a second image portion of the image of the traffic location; and

determining a duration for a green traffic light based at least in part upon the traffic density and the second traffic density.

17. The machine readable medium of claim 16, further comprising instructions that, when executed, cause the machine to monitor traffic by:

comparing the duration for the green traffic light to a maximum green light duration; and

if the duration for the green traffic light is less than the maximum green light duration, then comparing the traffic density to a minimum traffic density; and

if the traffic density is less than the minimum traffic density, then generating a green traffic light off signal.

18. The machine readable medium of claim 17, further comprising instructions that, when executed, cause the machine to monitor traffic by:

if the traffic density is greater than the minimum traffic density, then receiving a second image of the traffic location.

22. An apparatus comprising:

a machine readable medium having stored therein a plurality of programming instructions that, when executed, cause the apparatus to monitor traffic by;

receiving an image of a traffic location, the image of the traffic location including an image portion;

generating an image mask, the image mask being dimensionally representative of the image portion;

masking the image of the traffic location using the image mask to obtain the image portion, the image portion including an image object;

identifying the image object from the image portion; and

determining a traffic condition representative of the image portion based at least in part on the identified image object; and

a processor coupled to the machine readable medium to execute the programming instructions.

23. The apparatus of claim 22, wherein the traffic condition comprises a traffic density.

24. The machine readable medium of claim 23, wherein determining the traffic density comprises:

detecting an edge of the image object;

generating an image-bounding box around the edge of the image object;

translating the image-bounding box from a perspective coordinate frame to a world coordinate frame;

determining an area of the image-bounding box in the world coordinate frame;

determining an area of the image portion in the world coordinate frame; and

determining the traffic density as a ratio of the area of the image-bounding box in the world coordinate frame to the area of the image portion in the world coordinate frame.

25. The machine readable medium of claim 24, further comprising instructions that, when executed, cause the machine to monitor traffic by:

enhancing the edge of the image object by applying a morphological function.

26. The machine readable medium of claim 23, further comprising instructions that, when executed, cause the machine to monitor traffic by:

determining a second traffic density for a second image portion of the image of the traffic location; and

determining a duration for a green traffic light based at least in part upon the traffic density and the second traffic density.

27. The machine readable medium of claim 26, further comprising instructions that, when executed, cause the machine to monitor traffic by:

comparing the duration for the green traffic light to a maximum green light duration; and

if the duration for the green traffic light is less than the maximum green light duration, then comparing the traffic density to a minimum traffic density; and

if the traffic density is less than the minimum traffic density, then generating a green traffic light off signal.

28. The machine readable medium of claim 27, further comprising instructions that, when executed, cause the machine to monitor traffic by:

if the traffic density is greater than the minimum traffic density, then obtaining a second image of the traffic location.

29. The machine readable medium of claim 22, further comprising instructions that, when executed, cause the machine to monitor traffic by:

generating control signals for transmission to a traffic station for enabling traffic control.

30. The machine readable medium of claim 22, wherein generating the image mask comprises:

identifying a plurality of lines within the traffic image;

identifying a first line and a second line from the plurality of lines, the first line and the second line being the two longest of the plurality of lines; and

defining the image mask between the first line and the second line, and a portion of a boundary of the traffic image.

31. The machine readable medium of claim 30, wherein identifying the plurality of lines comprises of Hough transform.