Method for recognizing road signs by PSO-SVM (particle swarm optimization-support vector machine) based on GPU (graphics processing unit)

A road and sign technology, which is applied in the recognition of road speed limit signs by self-adaptive mutation particle swarm optimization support vector machine, and the field of road speed limit sign recognition by support vector machine. The effect of quick identification

Active Publication Date: 2013-03-27
CHONGQING UNIV OF POSTS & TELECOMM
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To solve the problem of slow computing speed, most of them use parallel processing methods

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  • Method for recognizing road signs by PSO-SVM (particle swarm optimization-support vector machine) based on GPU (graphics processing unit)
  • Method for recognizing road signs by PSO-SVM (particle swarm optimization-support vector machine) based on GPU (graphics processing unit)
  • Method for recognizing road signs by PSO-SVM (particle swarm optimization-support vector machine) based on GPU (graphics processing unit)

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[0020] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0021] For example, the sample data set consists of four types of common speed limit signs on domestic roads (20, 40, 60, 80km / h), and the features of the successfully extracted images are used as the experimental data set. For example, 64 samples in the experimental data set can be randomly selected as the training set, and the remaining samples are used as samples to be identified. The feature vector of the data set is 35 dimensions. Using the GPU-ALTMPSO algorithm to optimize SVM parameters and build a road sign recognition classifier, it can identify the above-mentioned types of road speed limit signs. Compared with the existing road speed limit sign recognition methods, the accuracy of road speed limit sign recognition is improved.

[0022] refer to figure 1 , to perform feature extraction on road spee...

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Abstract

The invention discloses a method for recognizing road speed limit signs by optimizing an SVM (support vector machine) by adaptive mutation particle swarm optimization based on a GPU (graphics processing unit). The category of the road speed limit signs is quickly and accurately recognized by using PSO (particle swarm optimization) to optimize parameters of an SVM. Based on the characteristics of particle swarm in optimizing parameters of the SVM, such as high data processing capacity and long computation time, the operating speed of PSO algorithm is increased by parallel computing of the GPU. The method has the advantages that the method of optimizing the support vector machine by GPU-accelerated ALTM PSO (adaptive local tone mapping-particle swarm optimization) is superior to the traditional SVM in accuracy of recognizing road speed limit signs and is superior to the standard PSO-SVM in algorithm convergence and operating speed.

Description

technical field [0001] The invention relates to a support vector machine road speed limit sign recognition method, which belongs to the field of intelligent transportation, and in particular to a road speed limit sign recognition method using a GPU to realize adaptive variation particle swarm optimization support vector machine. Background technique [0002] Road speed limit sign recognition is an important branch of intelligent transportation research, which has two functions: first, to manage traffic; second, to guide and warn drivers. The automatic recognition of road speed limit signs is conducive to improving the active safety performance of vehicles. Road speed limit sign recognition mainly includes two basic technical links: (1) speed limit sign detection, including speed limit sign positioning and image preprocessing; (2) speed limit sign recognition, including speed limit sign feature extraction with categories. [0003] With the development of computing intellige...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/62
Inventor 王进熊虎陶树人
Owner CHONGQING UNIV OF POSTS & TELECOMM
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