Maximally stable extremal region and SVM based traffic sign recognition method

A maximum stable extremum and traffic sign recognition technology, applied in the field of image processing, to achieve the effect of avoiding error, balancing accuracy and real-time performance, and reducing interference

Inactive Publication Date: 2017-11-07
深圳市美好幸福生活安全系统有限公司
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Problems solved by technology

Therefore, in view of the disadvantages of color recognition in complex environments, the error-proneness of manual marking and the time-consuming machine training, and t

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  • Maximally stable extremal region and SVM based traffic sign recognition method
  • Maximally stable extremal region and SVM based traffic sign recognition method
  • Maximally stable extremal region and SVM based traffic sign recognition method

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Embodiment Construction

[0013] The specific implementation manners of the present invention will be further described below in conjunction with the accompanying drawings and technical solutions.

[0014] For the above three steps, the following specific instructions are given for each step:

[0015] Step 1: Color Conversion

[0016] Image normalization: Since traffic signs are mostly red and blue, first use the formula

[0017]

[0018] Perform statistics on the red and blue parts, select the larger one as the threshold, and perform red / blue normalization processing on the picture.

[0019] Step 2: Edge Detection

[0020] 2.1 Calculate the image gradient. In the image, where the gray value of adjacent pixels changes little, the gradient amplitude is relatively small; on the contrary, where the gray level changes suddenly, the gradient amplitude is large. Therefore, it is necessary to obtain the magnitude of the magnitude through the first-order reciprocal calculation to determine the position ...

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Abstract

The invention discloses a maximally stable extremal region and SVM based traffic sign recognition method, which is characterized in that a maximally stable extremal region (MSER) algorithm is adopted to detect a traffic sign part in an RGB image, graying treatment is performed on the image, and the stability of the MSER algorithm is brought into play. The method applies an HOG eigenvector to act as a method for edge detection and image segmentation for a region to be identified, thereby being capable of suppressing influences brought about by translation and rotation to a certain extent. The method is insensitive to changes of illumination, so that the interference brought by changes in illumination intensity to the image can also be reduced. An SVM classifier is used in the stage of classification and recognition to avoid easy error occurrence performance of manual marking and great time consumption of machine training. The method well balances the requirements of accuracy and timeliness, and realizes automatic detection and recognition for traffic signs. The method performs recognition on test images in a German traffic sign detection benchmark database and acquires good effects.

Description

technical field [0001] The invention belongs to the field of image processing and is applied in intelligent traffic scenes, and relates to the application of the maximum stable extremum region (MSER) ​​algorithm to realize the effective processing of the traffic sign part in the RGB image, and the application of the direction gradient histogram (HOG) to the region of interest. Edge detection and segmentation are finally sent to the vector machine classifier (SVM) to complete the work of the recognition stage. Background technique [0002] Image processing technology is an important part of the field of intelligent transportation. Efficient and accurate automatic traffic sign recognition can guide traffic participants to participate in traffic behavior in a standardized manner, reduce the pressure of driver information processing, and thereby reduce the probability of accidents. At present, the traffic sign recognition (Traffic Sign Recognition, TSR) system mainly collects th...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06T7/13G06T7/90
CPCG06T7/13G06T7/90G06T2207/30256G06T2207/10024G06V20/582G06F18/2411
Inventor 高振国钱坤陈丹杰陈炳才卢志茂姚念民
Owner 深圳市美好幸福生活安全系统有限公司
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