Vehicle license plate recognition method based on extremal regions and extreme learning machine

Abstract

The invention discloses a vehicle license plate recognition method based on extremal regions and an extreme learning machine. The method includes the steps that color images to be processed are preprocessed, vehicle license plate regions are roughly positioned, and multiple vehicle license plate candidate regions are obtained; based on the vehicle license plate candidate regions, the extremal regions of RGB color channels are extracted from the color images to be processed, the extremal regions according with the geometric attributes of vehicle license plate character regions are selected from a classifier, and the vehicle license plate character regions are obtained; a single implicit strata feedforward neural network based on the extreme learning machine is established through supervised learning, characteristic vectors of the character regions are extracted as input, and vehicle license plate characters are automatically recognized through the neutral network. The method has the advantages of being high in speed and precision and the like and can well deal with adverse factors such as complex backgrounds, weather changes, illumination influence and the like particularly in complex traffic environments. The defects of a traditional vehicle license plate recognition method in real time performance and robustness are overcome, and the method has significant application value.

Description

technical field [0001] The invention relates to the technical fields of intelligent video monitoring and intelligent transportation, in particular to a license plate recognition method based on extreme value regions and extreme learning machines, which can be applied to license plate recognition in complex traffic environments. Background technique [0002] With the development of video surveillance technology, intelligent monitoring of traffic scenes has become an important part of realizing traffic intelligence. At present, in terms of intelligent monitoring of traffic scenes, the license plate number, as a unique identity representation of motor vehicles, is an extremely important traffic information, and automatic license plate recognition is an indispensable function in intelligent transportation systems. Automatic license plate recognition has the functions of monitoring, recording, verifying and alarming vehicles, and can be applied to parking lot charges, community v...

AI Technical Summary

Problems solved by technology

For example, in the paper "Component-based license plate detection using conditional random field model" published by Li Bo et al. on IEEE Transactions on Intelligent Transportation Systems in 2013, the appropriate maximum stable extremum region was extracted in the gray-scale domain, and the conditional random field model was used. The airport method is used to model, segment and extract character areas. This method is only processed in the gray-scale domain, which is sensitive to illumination changes in traffic scenes and is easy to misdetect or miss license plate characters; Ying Wen et al. in 2011 IEEE Transactions on The paper "An algorithm for license plate recognition applied to intelligent transportation system" published on Intelligent Transportation Systems calculates the horizontal and vertical projections of local areas to segment characters, and then uses support vector machines to recognize characters. This method is easily affected by shadows. Influence, poor robustness, and training support vector machines is very time-consuming

[0005] From the development status of existing technologies, it can be seen that automatic license plate recognition in complex traffic scenes is still an unsolved problem.

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