Object classification method for a collision warning system

• US 20060153459A1
• Filed: 01/10/2005
• Published: 07/13/2006
• Est. Priority Date: 01/10/2005
• Status: Abandoned Application

• Current Assignee: Delphi Technologies, Inc. (BorgWarner Incorporated)
• Original Assignee: Delphi Technologies, Inc. (BorgWarner Incorporated)
• Inventors: Zhang, Yan, Kiselewich, Stephen J.
• Application Number:
  US11/032,629
• Publication Number:
  US 20060153459A1
• Time in Patent Office: Days
• US Class Current: 382/224
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes

Abstract

An object classification method for a collision warning system is disclosed. The method includes the steps of capturing a video frame with an imaging device and examining a radar-cued potential object location within the video frame, extracting orthogonal moment features from the potential object location, extracting Gabor filtered features from the potential object location, and classifying the potential object location into one of a first type of image or a second type of image in view of the extracted orthogonal moment features and the Gabor filtered features.

Claims

1. An object classification method comprising the steps of:

capturing a video frame with an imaging device and examining a radar-cued potential object location within the video frame;

extracting orthogonal moment features from the potential object location;

extracting Gabor filtered features from the potential object location; and

classifying the potential object location into one of a first type of image or a second type of image in view of the extracted orthogonal moment features and the Gabor filtered features.

2. The object classification method according to claim 1, wherein the classifying step is conducted in view of a merging of the extracted orthogonal moment features and the Gabor filtered features.

3. The object classification method according to claim 1, wherein the capturing step further comprising the step of sub-dividing the potential object location into more than one sub-region.

4. The object classification method according to claim 3, wherein the extracting orthogonal moment features step further comprises extracting orthogonal moment features from each of the one or more sub-regions.

5. The object classification method according to claim 1, wherein the orthogonal moment features are orthogonal Legendre moment features.

6. The object classification method according to claim 1, wherein the orthogonal moment features are orthogonal Zernike moment features.

7. The object classification method according to claim 1, wherein the Gabor filtered features are defined to include two scales/resolution and four directions defined by a 0°

, a 45°

, a 90°

, and a 135°

orientation.

8. The object classification method according to claim 7, wherein the Gabor filtered feature further comprises nine overlapping 20×

20 pixel sub-regions and three texture metrics including mean, standard deviation, and skewness.

9. The object classification method according to claim 1, wherein the classifying step is conducted by a support vector machine or a neural network.

10. An object classification method for a collision warning system comprising the steps of:

capturing a video frame with an imaging device and examining a radar-cued potential object location within the video frame;

extracting orthogonal Legendre moment features from the potential object location;

extracting Gabor filtered features from the potential object location; and

classifying the potential object location into one of a vehicle image or a non-vehicle image in view of a merging of the extracted orthogonal Legendre moment features and the Gabor filtered features.

11. The object classification method according to claim 10, wherein the capturing step further comprising the step of sub-diving the potential object location into more than one sub-region.

12. The object classification method according to claim 11, wherein the extracting orthogonal Legendre moment features step further comprising extracting orthogonal Legendre moment features from each of the one or more sub-regions.

13. The object classification method according to claim 10, wherein the Gabor filtered features are defined to include two scales/resolution and four directions defined by a 0°

, a 45°

, a 90°

, and a 135°

orientation.

14. The object classification method according to claim 13, wherein the Gabor filtered feature further comprises nine overlapping 20×

20 pixel sub-regions and three texture metrics including mean, standard deviation, and skewness.

15. The object classification method according to claim 10, wherein the classifying step is conducted by a support vector machine or a neural network.

16. An object classification method for a collision warning system comprising the steps of:

capturing a video frame with an imaging device and examining a radar-cued potential object location within the video frame;

extracting orthogonal Legendre moment features from the potential object location;

extracting Gabor filtered features from the potential object location;

extracting supplemental image features from the potential object location; and

classifying the potential object location into one of a vehicle image or a non-vehicle image in view of the extracted orthogonal Legendre moment features, the Gabor filtered features, and the supplemental image features.

17. The object classification method for a collision warning system according to claim 16, wherein the capturing step further comprising the step of sub-diving the potential object location into more than one sub-region.

18. The object classification method for a collision warning system according to claim 16, wherein the extracting orthogonal Legendre moment features step further comprising extracting orthogonal Legendre moment features from each of the one or more sub-regions.

19. The object classification method for a collision warning system according to claim 16, wherein the Gabor filtered features are defined to include two scales/resolution and four directions defined by a 0°

, a 45°

, a 90°

, and a 135°

orientation.

20. The object classification method for a collision warning system according to claim 19, wherein the Gabor filtered featured further comprises nine overlapping 20×

20 pixel sub-regions and three texture metrics including mean, standard deviation, and skewness.

21. The object classification method for a collision warning system according to claim 16, wherein the classifying step is conducted by a support vector machine or a neural network.

22. The object classification method for a collision warning system according to claim 16, wherein the extracting supplemental image features from the potential object location step includes Haar wavelets and edge features.

23. The object classification method for a collision warning system according to claim 16, wherein the extracting supplemental image features from the potential object location step includes orthogonal Zernike moments.