[1]JIANG Xinhua,GAO Sheng,LIAO Ljuchao,et al.Traffic video vehicle detection based on semi-supervised SVM classification algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(5):690-698.[doi:10.11992/tis.201406044]
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Traffic video vehicle detection based on semi-supervised SVM classification algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 5 Page number: 690-698 Column: 学术论文—机器学习 Public date: 2015-10-25
Title:
Traffic video vehicle detection based on semi-supervised SVM classification algorithm
Author(s):
JIANG Xinhua1; 2;  GAO Sheng3;  LIAO Ljuchao1; 2;  ZOU Fumin2
1. School of Information Science and Engineering, Central South University, Changsha 410075, China;
2. Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350108, China;
3. School of Software Engineering, Central South University, Changsha 410075, China
Keywords:
vehicle detection; histograms of oriented gradients (HOG) feature; local binary pattern (LBP) feature; support vector machine (SVM) classifier; semi-supervised learning; motion region
CLC:
TP181
DOI:
10.11992/tis.201406044
Abstract:
This paper presents a kind of traffic video vehicle detection method based on a semi-supervised support vector machine (SVM) classification algorithm to improve accuracy and enhance adaptability of vehicle counting in the traffic scene. By analyzing a small number of artificially labeled samples, two SVM classifiers with different kernels are trained on the basis of histograms of oriented gradients (HOG) features and local binary pattern (LBP) features, respectively. A semi-supervised SVM (SEMI-SVM) for classification is proposed by adopting the thoughts of semi learning. Then the unknown samples are labeled and added into the original sample database. The proposed method supports data update of the dynamic sample database, avoids heavy manual work labeling samples and enhances adaptability of the algorithm. A motion region is extracted using the three-frame difference rule. The classifier is then loaded to make a multi-scale detection in the extracted motion region, and moving vehicles are marked and counted. The results show the algorithm has good response, good adaptability, and the detection accuracy of moving vehicles is much improved, even under the complex traffic circumstances.
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