[1]蒋新华,高晟,廖律超,等.半监督SVM分类算法的交通视频车辆检测方法[J].智能系统学报编辑部,2015,10(5):690-698.[doi:10.11992/tis.201406044]
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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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]
半监督SVM分类算法的交通视频车辆检测方法
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
10
期数:
2015年第5期
页码:
690-698
栏目:
学术论文—机器学习
出版日期:
2015-10-25
- Title:
- Traffic video vehicle detection based on semi-supervised SVM classification algorithm
- Keywords:
- vehicle detection; histograms of oriented gradients (HOG) feature; local binary pattern (LBP) feature; support vector machine (SVM) classifier; semi-supervised learning; motion region
- 分类号:
- TP181
- 文献标志码:
- A
- 摘要:
- 针对交通场景运动车辆检测中车辆数目统计准确率不高、自适应性不强等问题,提出了一种基于半监督支持向量机(SVM)分类算法的交通视频车辆检测方法。利用人工标记的少量样本,分别训练2个基于方向梯度直方图(HOG)特征与基于局部二值模式(LBP)特征的不同核函数的SVM分类器;结合半监督算法的思想,构建SVM的半监督分类方法(SEMI-SVM),标记未知样本并加入到原样本库中,该方法支持样本库动态更新,避免了繁重的人工标记样本的工作,提高了自适应性;最后,通过三帧差分法提取运动区域,加载分类器在该区域进行多尺度检测,标记检测出来的运动车辆,统计车辆数目。实验结果表明:该方法在具有一定的自适应性的同时,有较高的车辆检测准确率,即使在复杂交通情况下,对运动车辆依然有很好的检测效果。
- 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.
- 参考文献/References:
-
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备注/Memo
收稿日期:2014-06-22;改回日期:。
基金项目:国家自然科学基金资助项目(61304199,41471333);福建省自然科学基金资助项目(2013J01214);福建省科技重大专项专题资助项目(2011HZ0002-1);福建省交通科技计划项目(201318),福建省教育厅B类科研项目(JB3213).
作者简介:蒋新华,男,1956年生,教授,博士生导师,福建工程学院校长,主要研究方向为控制理论应用、电力机车智能故障诊断技术、移动互联网关键技术和车联网关键技术。主持和参加铁道部、湖南省、福建省等重要科学研究项目30余项,发表学术论文100余篇;高晟,男,1989年生,硕士研究生,主要研究方向为交通信息分析及图像处理。参与国家自然科学基金资助项目1项,授权发明专利4项;廖律超,1980年生,工程师,博士研究生,主要研究方向为海量动态信息数据挖掘分析、交通信息处理关键技术。
通讯作者:高晟.E-mail:csugaosheng@163.com.
更新日期/Last Update:
2015-11-16