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Vehicle detection method

A vehicle detection and vehicle technology, applied in the field of target detection, can solve the problems of limited use range, unsatisfactory detection effect, large memory consumption, etc., and achieve the effects of improving training speed, increasing grayscale symmetry verification, and improving reliability.

Inactive Publication Date: 2018-03-30
NANJING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

The disadvantage of this type of algorithm is that it usually consumes a lot of memory because it usually needs to buffer several frames to learn the background, which limits its scope of use.
In addition, for large-scale background disturbances, the detection effect of such algorithms is not ideal

Method used

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Embodiment Construction

[0064] to combine figure 1 , a vehicle detection method based on structural Haar features and Adaboost algorithm, comprising the following steps:

[0065] Step 1: Construct structural Haar features, add five basic Haar features to form a new feature library, and use the feature library to extract sample feature values;

[0066] Step 2, use the Adaboost algorithm to train the classifier, extract the adaptive classification threshold, and obtain the best weak classifier;

[0067] Step 3, multiple iterations to train multiple weak classifiers, and weighted average to form a strong classifier;

[0068] Step 4, train multiple strong classifiers to form a cascade classifier;

[0069] Step 5, use cascaded classifiers for vehicle detection, and verify the gray symmetry of the initial detection results, and classify and merge the verified results to obtain the final detection results.

[0070] Step 1: Construct structural Haar features, add five basic Haar features to form a new fea...

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Abstract

The invention provides a vehicle detection method based on structural Haar features and Adaboost algorithm. The vehicle detection method includes the steps of constructing the structural Haar featuresof vehicles and forming a new feature library with basic Haar features, and using the feature library to extracting sample eigenvalues; training a classifier through the Adaboost algorithm to extractan adaptive classification threshold and obtain a best weak classifier; conducting multiple iterations to train multiple weak classifiers and performing weighted average to obtain a strong classifier; training a plurality of strong classifiers and forming a cascade classifier; carrying out vehicle detection through the cascade classifier, verifying gray level symmetry of initial test results, andacquiring a final detection result through classification and combination of the results after verification.

Description

technical field [0001] The invention relates to a target detection technology, in particular to a vehicle detection method based on structural Haar features and Adaboost algorithm. Background technique [0002] Intelligent traffic management system is the development trend of road traffic management in the 21st century. The continuous and rapid development of expressways and the continuous improvement of the vehicle management system provide an opportunity for the intelligent traffic management system to enter the field of practical application. As a key technology in intelligent transportation, vehicle detection technology has broad application prospects in daily life. Intelligent transportation has strict requirements for real-time and accuracy of video vehicle detection technology. When detecting, the complex background and various interferences in the video image are the problems faced by the current video vehicle detection technology. With the continuous efforts of d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/2413
Inventor 刘磊陈旭宋佳晓张壮李业飞赵如雪
Owner NANJING UNIV OF SCI & TECH
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