CAJ | 학술논문

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.
침대교통장경운동차량검측중차량수목통계준학솔불고、자괄응성불강등문제,제출료일충기우반감독지지향량궤( SVM)분류산법적교통시빈차량검측방법. 이용인공표기적소량양본,분별훈련2개기우방향제도직방도(HOG)특정여기우국부이치모식(LBP)특정적불동핵함수적SVM분류기;결합반감독산법적사상,구건SVM적반감독분류방법( SEMI-SVM) ,표기미지양본병가입도원양본고중,해방법지지양본고동태경신,피면료번중적인공표기양본적공작,제고료자괄응성;최후,통과삼정차분법제취운동구역,가재분류기재해구역진행다척도검측,표기검측출래적운동차량,통계차량수목. 실험결과표명:해방법재구유일정적자괄응성적동시,유교고적차량검측준학솔,즉사재복잡교통정황하,대운동차량의연유흔호적검측효과.
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 ker-nels 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 en-hances adaptability of the algorithm. A motion region is extracted using the three-frame difference rule. The classifi-er 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.