[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]
点击复制

半监督SVM分类算法的交通视频车辆检测方法(/HTML)
分享到:

《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年5期
页码:
690-698
栏目:
出版日期:
2015-10-25

文章信息/Info

Title:
Traffic video vehicle detection based on semi-supervised SVM classification algorithm
作者:
蒋新华12 高晟3 廖律超12 邹复民2
1. 中南大学 信息科学与工程学院, 湖南 长沙 410075;
2. 福建工程学院 福建省汽车电子与电驱动技术重点实验室, 福建 福州 350108;
3. 中南大学 软件学院, 湖南 长沙 410075
Author(s):
JIANG Xinhua12 GAO Sheng3 LIAO Ljuchao12 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
关键词:
车辆检测HOG特征LBP特征SVM分类器半监督学习运动区域
Keywords:
vehicle detectionhistograms of oriented gradients (HOG) featurelocal binary pattern (LBP) featuresupport vector machine (SVM) classifiersemi-supervised learningmotion region
分类号:
TP181
DOI:
10.11992/tis.201406044
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:

[1] XIONG Changzhen, FAN Wuyi, LI Zhengxi. Traffic flow detection algorithm based on intensity curve of high-resolution image[C]//IEEE Computer Modeling and Simulation.Sanya, China, 2010:159-162.
[2] MARIN D, AQUINO A, GEGU’NDEZ-ARIAS M E, et al. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features[J]. IEEE Transactions on Medical Imaging, 2011, 30(1):146-158.
[3] HAN B, DAVIS L S. Density-based multifeature background subtraction with support vector machine[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(5):1017-1023.
[4] CHENG Li, GONG Minglun, SCHUURMANS D, et al. Real-time discriminative background subtraction[J]. IEEE Transactions on Image Processing, 2011, 20(5):1401-1414.
[5] BARNICH O, VAN DROOGENBROECK M. ViBe:A universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6):1709-1724.
[6] FU Wenlong, JOHNSTON M, ZHANG Mengjie. Genetic programming for edge detection:a global approach[C]//2011 IEEE Congress on Evolutionary Computation. Wellington, New Zealand, 2011:254-261.
[7] Al-GHAILI A M, MASHOHOR S, RAMLI A R, et al. Vertical-edge-based car-license-plate detection method[J]. IEEE Transactions on Vehicular Technology, 2013, 62(1):26-38.
[8] 钱志明, 杨家宽, 段连鑫. 基于视频的车辆检测与跟踪研究进展[J]. 中南大学学报:自然科学版, 2013, 44(S2):222-227. QIAN Zhiming, YANG Jiakuan, DUAN Lianxin. Research advances in video-based vehicle detection and tracking[J]. Journal of Central South University:Science and Technology, 2013,44(S2):222-227.
[9] CHEN Xueyun, XIANG Shiming, LIU Chenglin, et al. Vehicle detection in satellite images by hybrid deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10):1797-1801.
[10] ZHANG Bailing. Reliable classification of vehicle types based on cascade classifier ensembles[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1):322-332.
[11] CHENG H Y, WENG C C, CHEN Yiying. Vehicle detection in aerial surveillance using dynamic Bayesian networks[J]. IEEE Transactions on Image Processing, 2012, 21(4):2152-2159.
[12] CARAFFI C, VOJIR T, TREFNY J, et al. A system for real-time detection and tracking of vehicles from a single car-mounted camera[C]//201215th International IEEE Conference on Intelligent Transportation Systems. Anchorage, USA, 2012:975-982.
[13] CAO Xianbin, WU Changxia, YAN Pingkun, et al. Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos[C]//201118th IEEE International Conference on Image Processing. Hefei, China, 2011:2421-2424.
[14] DARNSTÄDT M, SIMON H U, SZÖRÉNYI B. Supervised learning and co-training[J]. Theoretical Computer Science, 2014, 519:68-87.
[15] 刘杨磊, 梁吉业, 高嘉伟, 等. 基于Tri-training的半监督多标记学习算法[J]. 智能系统学报, 2013, 8(5):439-445. LIU Yanglei, LIANG Jiye, GAO Jiawei, et al. Semi-supervised multi-label learning algorithm based on Tri-training[J]. CAAI Transactions on Intelligent Systems, 2013, 8(5):439-445.
[16] WANG Xiaoyu, HAN T X, YAN Shuicheng. An HOG-LBP human detector with partial occlusion handling[C]//2009 IEEE 12th International Conference on Computer Vision. Kyoto, Japan, 2009:32-39.
[17] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005:886-893.
[18] CHANG C C, LIN C J. LIBSVM:a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3):27.
[19] 钱晓山, 阳春华. 基于GEP的最小二乘支持向量机模型参数选择[J]. 智能系统学报, 2012, 7(3):225-229. QIAN Xiaoshan, YANG Chunhua. A parameter selection method of a least squares support vector machine based on gene expression programming[J]. CAAI Transactions on Intelligent Systems, 2012, 7(3):225-229.
[20] BEN-HUR A, WESTON J. A user’s guide to support vector machines[M]//Data Mining Techniques for the Life Sciences. Washington,DC:Humana Press, 2010:223-239.
[21] 胡光龙, 秦世引. 动态成像条件下基于SURF和Mean shift的运动目标高精度检测[J]. 智能系统学报, 2012, 7(1):61-68. HU Guanglong, QIN Shiyin. High precision detection of a mobile object under dynamic imaging based on SURF and Mean shift[J]. CAAI Transactions on Intelligent Systems, 2012, 7(1):61-68.

相似文献/References:

[1]丁爱玲,杨康,齐怀超,等.单边侧阴影特征的车辆阴影去除[J].智能系统学报编辑部,2015,10(02):281.[doi:10.3969/j.issn.1673-4785.201310020]
 DING Ailing,YANG Kang,QI Huaichao,et al.Vehicle shadow removal based on the characteristics of the single side shadow[J].CAAI Transactions on Intelligent Systems,2015,10(5):281.[doi:10.3969/j.issn.1673-4785.201310020]
[2]史东承,倪康.压缩感知W-HOG特征的运动手势跟踪[J].智能系统学报编辑部,2016,11(1):124.[doi:10.11992/tis.201507005]
 SHI Dongcheng,NI Kang.Motion gesture tracking based on compressed sensing W-HOG features[J].CAAI Transactions on Intelligent Systems,2016,11(5):124.[doi:10.11992/tis.201507005]
[3]姜英,王延江.REM记忆模型在图像分类识别中的应用[J].智能系统学报编辑部,2017,12(03):310.[doi:10.11992/tis.201605010]
 JIANG Ying,WANG Yanjiang.Application of REM memory model in image recognition and classification[J].CAAI Transactions on Intelligent Systems,2017,12(5):310.[doi:10.11992/tis.201605010]
[4]徐志通,骆炎民,柳培忠.联合加权重构轨迹与直方图熵的异常行为检测[J].智能系统学报编辑部,2018,13(06):1015.[doi:10.11992/tis.201706070]
 XU Zhitong,LUO Yanmin,LIU Peizhong.Abnormal behavior detection of joint weighted reconstruction trajectory and histogram entropy[J].CAAI Transactions on Intelligent Systems,2018,13(5):1015.[doi:10.11992/tis.201706070]
[5]王科平,蔡凯利,王红旗,等.一种基于雨线主方向自适应的全局稀疏去雨模型[J].智能系统学报编辑部,2020,15(2):271.[doi:10.11992/tis.201809042]
 WANG Keping,CAI Kaili,WANG Hongqi,et al.A global sparse rain removal model based on rain streaks main direction adaptation[J].CAAI Transactions on Intelligent Systems,2020,15(5):271.[doi:10.11992/tis.201809042]

备注/Memo

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);福建省高校杰出青年科研人才计划(JA14209);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