Traffic light detection

Patent metadata

Field	Value
Publication number	US-9953229-B2
Application number	US-201314901651-A
Country	US
Kind code	B2
Filing date	Aug 20, 2013
Priority date	Aug 20, 2013
Publication date	Apr 24, 2018
Grant date	Apr 24, 2018

Abstract

A method and a system for traffic light detection are provided. The method may include: obtaining a color image; calculating pixel response values for pixels of the color image, respectively, where each of the pixel response values may be calculated using R, G, and B values of a corresponding pixel directly, such that pixel response values of red traffic light pixels are substantially distributed on a first side of a predetermined range and pixel response values of green traffic light pixels are substantially distributed on a second side of the predetermined range which is opposite to the first side; identifying pixels whose pixel response values are distributed on the first side or the second side as candidate pixels; identifying candidate blobs based on the candidate pixels; and verifying whether the candidate blobs are traffic lights. Efficiency and reliability may be improved.

First claim

We claim:

1. A method for traffic light detection, comprising:
obtaining, with a camera, a color image;
calculating, with a processor, pixel response values for pixels of the color image, respectively, where each of the pixel response values is calculated using R, G, and B values of a corresponding pixel directly, such that pixel response values of red traffic light pixels and pixel response values of green traffic light pixels are substantially distributed outside of a predetermined range, where the pixel response values of red traffic light pixels are substantially distributed on a first side of the predetermined range and the pixel response values of green traffic light pixels are substantially distributed on a second side of the predetermined range which is opposite to the first side, each of the pixel response values being calculated based on multiplication of a first component and a second component of a corresponding pixel, the second component being calculated based on a summation of a sixth component and a seventh component which are respectively based on R and G values of the corresponding pixel, where the sixth and seventh components have a same sign;
identifying, with the processor, pixels whose pixel response values are distributed on the first side or the second side as candidate pixels;
identifying, with the processor, candidate blobs based on the candidate pixels; and
verifying, with the processor, whether the candidate blobs are traffic lights,
wherein the processor is configured to use a classifier to verify whether the candidate blobs are traffic lights, and
wherein the classifier is a neural network, a support vector machine, or a cascade detector.

2. The method according to claim 1 , wherein pixel response values of pixels other than red traffic light pixels and green traffic light pixels are substantially distributed within the predetermined range.

3. The method according to claim 1 , wherein the first component indicates a tendency of the pixel response value, and the second component is able to amplify the tendency if the tendency is towards the first side or the second side.

4. The method according to claim 3 , wherein the first component is calculated based on a summation of a third, a fourth, and a fifth component which are respectively based on R, G, and B values of the corresponding pixel, where the third component has a first sign, and the fourth and fifth components have a second sign which is opposite to the first sign.

5. The method according to claim 4 , wherein the third component is k1*R, the fourth component is −k2*G, and the fifth component is −k3*B, where k1, k2, and k3 are set such that the tendency is substantially towards the first side if the corresponding pixel is a red traffic light pixel and the tendency is substantially towards the second side if the corresponding pixel is a green traffic light pixel.

6. The method according to claim 1 , wherein the sixth component is k4*R and the seventh component is k5*G, where k4 and k5 are set such that the pixel response value is substantially on the first side if the corresponding pixel is a red traffic light pixel, and the pixel response value is substantially on the second side if the corresponding pixel is a green traffic light pixel.

7. The method according to claim 1 , wherein identifying the candidate blobs based on the candidate pixels comprises:
identifying a first set of blobs based on the candidate pixels; and
identifying the candidate blobs from the first set of blobs by determining, for each blob of the first set of blobs, whether or not the blob is a member of the candidate blobs based on pixel response values of pixels of the blob and surrounding pixels of the blob.

8. A system for traffic light detection, comprising a processing device configured to use a classifier, wherein the classifier is a neural network, a support vector machine, or a cascade detector, the system configured to:
obtain, from a camera, a color image;
calculate pixel response values for pixels of the color image, respectively, where each of the pixel response values is calculated using R, G, and B values of a corresponding pixel directly, such that pixel response values of red traffic light pixels and pixel response values of green traffic light pixels are substantially distributed outside of a predetermined range, where the pixel response values of red traffic light pixels are substantially distributed on a first side of the predetermined range and pixel response values of green traffic light pixels are substantially distributed on a second side of the predetermined range which is opposite to the first side, each of the pixel response values being calculated based on multiplication of a first component and a second component of a corresponding pixel, the second component being calculated based on a summation of a sixth component and a seventh component which are respectively based on R and G values of the corresponding pixel, where the sixth and seventh components have a same sign;
identify pixels whose pixel response values are distributed on the first side or the second side as candidate pixels;
identify candidate blobs based on the candidate pixels; and
verify, with the classifier, whether the candidate blobs are traffic lights.

9. The system according to claim 8 , wherein pixel response values of pixels other than red traffic light pixels and green traffic light pixels are substantially distributed within the predetermined range.

10. The system according to claim 8 , wherein the first component indicates a tendency of the pixel response value, and the second component is able to amplify the tendency if the tendency is towards the first side or the second side.

11. The system according to claim 10 , wherein the first component is calculated based on a summation of a third, a fourth, and a fifth component which are respectively based on R, G, and B values of the corresponding pixel, where the third component has a first sign, and the fourth and fifth components have a second sign which is opposite to the first sign.

12. The system according to claim 11 , wherein the third component is k1*R, the fourth component is −k2*G, and the fifth component is −k3*B, where k1, k2, and k3 are set such that the tendency is substantially towards the first side if the corresponding pixel is a red traffic light pixel and the tendency is substantially towards the second side if the corresponding pixel is a green traffic light pixel.

13. The system according to claim 8 , wherein the sixth component is k4*R and the seventh component is k5*G, where k4 and k5 are set such that the pixel response value is substantially on the first side if the corresponding pixel is a red traffic light pixel, and the pixel response value is substantially on the second side if the corresponding pixel is a green traffic light pixel.

14. The system according to claim 8 , wherein the processing device is configured to identify the candidate blobs by:
identifying a first set of blobs based on the candidate pixels; and
identifying the candidate blobs from the first set of blobs by determining, for each blob of the first set of blobs, whether or not the blob is a member of the candidate blobs based on pixel response values of pixels of the blob and surrounding pixels of the blob.

15. A system for traffic light detection, comprising:
a processor configured to use a classifier, wherein the classifier is a neural network, a support vector machine, or a cascade detector; and
a non-transitory computer readable medium storing instructions executable by the processor to:
calculate pixel res

Assignees

• Hong Chuyang
• Zhang Yankun
• Jiang Ruyi
• Zhou Yun
• Harman Int Ind

Inventors

• Hong Chuyang
• Zhang Yankun
• Jiang Ruyi
• Zhou Yun

Classifications

• G08G1/09623 Primary

  Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle (G08G1/0967 takes precedence) · CPC title

• G06V20/584

  of vehicle lights or traffic lights · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06T7/90

  Determination of colour characteristics · CPC title

• G06K9/4647

  Physics · mapped topic