APPARATUS AND METHOD FOR RECOGNIZING TRAFFIC SIGN BOARD

• US 20150193663A1
• Filed: 07/15/2014
• Published: 07/09/2015
• Est. Priority Date: 01/06/2014
• Status: Active Grant

• Current Assignee: Hyundai Mobis Co., Ltd.
• Original Assignee: Hyundai Mobis Co., Ltd.
• Inventors: OH, Huen, BYUN, Hye Ran, LEE, Tae Woo, LIM, Kwang Yong
• Granted Patent: US 9,501,703 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/2148 characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06V 10/36 Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given point; Non-linear local filtering operations, e.g. median filtering
• CPC Class Codes: G06V 10/467 Encoded features or binary features, e.g. local binary patterns [LBP]
• CPC Class Codes: G06V 10/7747 Organisation of the process, e.g. bagging or boosting
• CPC Class Codes: G06V 20/582 of traffic signs

Abstract

A method and apparatus for detecting and recognizing a traffic sign using a modified census transform (MCT) feature are disclosed. The traffic sign recognizing method according to an exemplary embodiment of the present invention includes detecting a traffic sign candidate region from an input image using a modified census transform (MCT) feature; verifying whether the candidate region corresponds to a traffic sign using the MCT feature histogram for the candidate region; and lassifying a region of interest into the corresponding traffic sign step by step using the MCT feature histogram for the verified candidate region.

Claims

1. A traffic sign recognizing method, comprising:

detecting a traffic sign candidate region from an input image using a modified census transform (MCT) feature;

verifying whether the candidate region corresponds to a traffic sign using the MCT feature histogram for the candidate region; and

classifying a region of interest into the corresponding traffic sign step by step using the MCT feature histogram for the verified candidate region (hereinafter, referred to as a region of interest).

2. The method of claim 1, wherein the detecting of a candidate region includes:

designating a kernel window having a predetermined size with respect to a pixel to be transformed in the input image; and

comparing an average of pixel values of the pixels included in the kernel window with individual pixel values to calculate n digit bits of MCT feature such that if the pixel value of the corresponding pixel is larger than the average of the pixel values, a value is given as 1 and if the pixel value of the corresponding pixel is smaller than the average, a value is given as 0.

3. The method of claim 1, wherein the detecting of a candidate region includes:

extracting the MCT feature for every pixel of the input image using the pixel value of the input image; and

classifying the candidate region from the input image using a classifier in which an MCT feature for the traffic sign is trained using at least one of an AdaBoost algorithm and a cascade algorithm.

4. The method of claim 1, wherein the detecting of a candidate region includes:

extracting the MCT feature for every pixel of the input image using the pixel value of the input image;

first classifying n features from the extracted MCT features using a classifier in which the MCT feature for the traffic sign is trained; and

second classifying m features, among remaining MCT features, excluding the n features.

5. The method of claim 1, wherein the verifying includes:

creating an MCT feature histogram for an MCT feature value and a frequency of the value using the MCT feature for each pixel of the candidate region; and

verifying the MCT feature histogram of the input image using a classifier in which the MCT feature histogram for the traffic sign is trained.

6. The method of claim 1, wherein the recognizing of a traffic sign includes matching the region of interest using a radial symmetry detection (RSD).

7. The method of claim 1, wherein the recognizing of a traffic sign includes classifying the region of interest as the traffic sign using a traffic sign to be classified for every level and a multilevel classification tree which defines a region of the region of interest in which an MCT feature histogram is created.

8. The method of claim 1, wherein the recognizing of a traffic sign includes creating a first MCT feature histogram in a first region of the region of interest in an N-th level and a second MCT feature histogram in a second region of the region of interest in an N+1-th level.

9. The method of claim 1, wherein the recognizing of a traffic sign includes:

a first step of classifying the region of interest into a speed sign and other signs; and

a second step of classifying the speed sign as the corresponding speed sign or the other signs as the other sign and non-recognized target signs.

10. A traffic sign recognizing apparatus comprising:

one or more processors and a nonvolatile memory in which a code which is executed by the processor is stored,

wherein the processor is configured to detect a traffic sign candidate region from an input image using a modified census transform (MCT) feature;

verify whether the candidate region corresponds to a traffic sign using the MCT feature histogram for the candidate region; and

classify a region of interest into the corresponding traffic sign step by step using the MCT feature histogram for the verified candidate region (hereinafter, referred to as a region of interest).

11. The apparatus of claim 10, wherein the processor is configured to designate a kernel window having a predetermined size with respect to a pixel to be transformed in the input image;

and

compare an average of pixel values of the pixels included in the kernel window with individual pixel values to calculate n digit bits of MCT feature such that if the pixel value of the corresponding pixel is larger than the average of the pixel value, a value is given as 1 and if the pixel value of the corresponding pixel is smaller than the average, a value is given as 0.

12. The apparatus of claim 10, wherein the processor is configured to extract the MCT feature for every pixel of the input image using the pixel value of the input image;

and

classify the candidate region from the input image using a classifier in which an MCT feature for the traffic sign is trained using at least one of an AdaBoost algorithm and a cascade algorithm.

13. The apparatus of claim 10, wherein the processor is configured to extract the MCT feature for every pixel of the input image using the pixel value of the input image;

classify n features from the extracted MCT features using a classifier in which the MCT feature for the traffic sign is trained as a first step; and

classify m features, among remaining MCT features, excluding the n features as a second step.

14. The apparatus of claim 10, wherein the processor is configured to create an MCT feature histogram for an MCT feature value and a frequency of the value using the MCT feature for each pixel of the candidate region;

and

verify the MCT feature histogram of the input image using a classifier in which the MCT feature histogram for the traffic sign is trained.

15. The apparatus of claim 10, wherein the processor is configured to match the region of interest using a radial symmetry detection (RSD).

16. The apparatus of claim 10, wherein the processor is configured to classify the region of interest as the traffic sign using a traffic sign to be classified step by step and a multilevel classification tree which defines a region of the region of interest in which an MCT feature histogram is created.

17. The apparatus of claim 16, wherein the processor is configured to create a first MCT feature histogram in a first region of the region of interest in an N-th level and a second MCT feature histogram in a second region of the region of interest in an N+1-th level.