Traffic sign recognition method based on spare self-encoding and sparse representation

Abstract

The invention discloses a traffic sign recognition method based on spare self-encoding and sparse representation. The traffic sign recognition method comprises the steps of: collecting pictures containing traffic signs, and dividing the pictures into l types manually; extracting blocks of each type of the pictures containing the traffic signs, and preprocessing the image blocks; training l sparse auto-encoders respectively by taking the preprocessed image blocks as training samples, and denoting weight dictionaries as D1, D2 , ... , Dl; extracting a block of a test picture with an unknown traffic sign, and constructing a test image block; calculating a sparse coefficient of the test image block under each weight dictionary by utilizing a sparse representation principle and adopting an OMP algorithm; and solving F norms between a reconstructed image block and the original image block, and selecting the dictionary type with the minimal F norm as a traffic sign recognition result of the test picture. The traffic sign recognition method adopts spare self-encoding to extract sufficient features of traffic signs automatically, completes recognition through calculating distance between a reconstructed sample and the test sample, and can achieve high recognition accuracy.

Description

technical field [0001] The invention relates to the application field of computer automatic control, in particular to a method for traffic sign recognition based on sparse self-encoding and sparse representation. Background technique [0002] Due to its safe, reliable and efficient characteristics, intelligent transportation system has been paid more and more attention by traffic management departments. Traffic sign recognition is an important part of intelligent transportation, and it plays an important role in many aspects such as unmanned driving, assisted driving, and traffic sign maintenance. The human visual system can easily recognize traffic signs, however, it is still a very challenging task for computers to recognize traffic signs. In real traffic scenes, due to weather changes, lighting conditions, partial occlusion, scale changes, background interference, and small inter-class distances between some different types of traffic signs, the research on traffic sign ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for traffic sign recognition based on sparse autoencoding and sparse representation, which can effectively solve the problems of insufficient feature extraction and high computational complexity caused by classifier design in existing recognition methods

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