This paper proposes an algorithm that extracts features of back side of the vehicle and detects the front vehicle in real-time by local feature tracking of vehicle in the continuous images.The features in back side of...This paper proposes an algorithm that extracts features of back side of the vehicle and detects the front vehicle in real-time by local feature tracking of vehicle in the continuous images.The features in back side of the vehicle are vertical and horizontal edges,shadow and symmetry.By comparing local features using the fixed window size,the features in the continuous images are tracked.A robust and fast Haarlike mask is used for detecting vertical and horizontal edges,and shadow is extracted by histogram equalization,and the sliding window method is used to compare both side templates of the detected candidates for extracting symmetry.The features for tracking are vertical edges,and histogram is used to compare location of the peak and magnitude of the edges.The method using local feature tracking in the continuous images is more robust for detecting vehicle than the method using single image,and the proposed algorithm is evaluated by continuous images obtained on the expressway and downtown.And it can be performed on real-time through applying it to the embedded system.展开更多
文摘针对人工跛行检测不够及时,难以发现突发中、重度跛行及轻度跛行行为的问题,该文提出了一种基于正态分布背景统计模型(normal background statistical model,NBSM)与局部循环中心补偿跟踪模型(local circulation center compensation track,LCCCT)和线性斜率最近邻分类(distilling data of KNN,DSKNN)技术的奶牛跛行检测方法。首先利用NBSM模型对奶牛序列图像中的目标奶牛像素区域进行分割,然后对得到的奶牛像素区域利用LCCCT模型提取目标奶牛身体前部像素区域,用其区域通过DSKNN模型提取目标奶牛的头部、颈部以及与颈连接的背部轮廓线拟合直线斜率数据,基于大样本视频序列帧数据将视频集制成轻度跛行、中重度跛行及正常等3类标签的斜率数据集。为了验证算法的有效性,对随机选取的18段奶牛视频进行了验证,其中正常奶牛、轻度跛行奶牛及中重跛行奶牛视频段各6段,获得头部、颈部及背部连接处的拟合直线斜率数据集。在未清洗的数据集上,分别利用SVM、Naive Bayes以及KNN分类算法进行了奶牛跛行的分类检测试验,试验结果表明,SVM与Naive Bayes跛行分类检测正确率均为82.78%,KNN奶牛跛行检测正确率为81.67%。将未清洗的数据集进行清洗后,3类算法的结果表明,KNN分类算法的跛行检测正确率达93.89%,高于SVM分类算法的91.11%及Naive Bayes分类算法的86.11%。上述结果表明通过头部、颈部及背部连接处的拟合直线斜率特性可以正确检测奶牛跛行,未清洗的数据经数据清洗后,KNN分类算法可以取得更好的检测结果。该研究结果对于奶牛跛行疾病的预防、诊断具有重要意义。
基金supported by the Brain Korea 21 Project in 2011 and MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Infor mation Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency)(NIPA-2011-C1090-1121-0010)
文摘This paper proposes an algorithm that extracts features of back side of the vehicle and detects the front vehicle in real-time by local feature tracking of vehicle in the continuous images.The features in back side of the vehicle are vertical and horizontal edges,shadow and symmetry.By comparing local features using the fixed window size,the features in the continuous images are tracked.A robust and fast Haarlike mask is used for detecting vertical and horizontal edges,and shadow is extracted by histogram equalization,and the sliding window method is used to compare both side templates of the detected candidates for extracting symmetry.The features for tracking are vertical edges,and histogram is used to compare location of the peak and magnitude of the edges.The method using local feature tracking in the continuous images is more robust for detecting vehicle than the method using single image,and the proposed algorithm is evaluated by continuous images obtained on the expressway and downtown.And it can be performed on real-time through applying it to the embedded system.
