决策树弱分类支持的卫星视频运动检测
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摘要
针对卫星视频的运动检测存在的局部伪运动虚警问题,提出一种决策树支持的局部有差别更新背景减除法。首先,对ViBe背景模型加以改进,在原模型基础上新增更新因子参数,建立初始的背景模型;其次,利用改进的模型进行运动检测,并对检测结果进行连通域标记,得到分割目标集;然后,根据目标特征属性建立决策树模型,并对分割目标集进行弱分类处理;最后,根据分类结果对背景模型样本和对应的更新因子进行局部有差别更新,进而实现"伪运动"虚警消除。利用SkySat和吉林一号视频数据进行实验,结果表明所提方法的效果和性能良好,检测率优于0.909,针对经典ViBe方法的误检测消除率优于90%。
For the satellite video motion detection, two issues are harmful to the subsequent application.One is the local parallax"pseudo-motion", and the other is"Ghost pseudo-motion", which may lead to serious error detection. For these two issues, a decision tree-supported local differentiated updating background subtraction method is proposed. Firstly, the initial background model is established. One other parameter,the updating factor, is added based on the ViBe model. Each pixel in the model has a unique dynamic updating factor. In the next place, we perform the pixel-wise motion detection based on the modified model. The detection results are segmented into targets by using the connected component analysis-labeling method.Afterwards, the decision tree model is established based on the target characteristics. And the targets can be weekly classified into different categories. In the end, the background model samples and the factor parameters are differentiated updated according to the categories results. Using SkySat and Jilin No. 1 video data to do experiments. The experimental results show that our proposed method has favourable effect and performance, with the recall rate of results superior to 0.909. Comparing with the classical ViBe method,the error detection removal rate is superior to 90%.
For the satellite video motion detection, two issues are harmful to the subsequent application.One is the local parallax"pseudo-motion", and the other is"Ghost pseudo-motion", which may lead to serious error detection. For these two issues, a decision tree-supported local differentiated updating background subtraction method is proposed. Firstly, the initial background model is established. One other parameter,the updating factor, is added based on the ViBe model. Each pixel in the model has a unique dynamic updating factor. In the next place, we perform the pixel-wise motion detection based on the modified model. The detection results are segmented into targets by using the connected component analysis-labeling method.Afterwards, the decision tree model is established based on the target characteristics. And the targets can be weekly classified into different categories. In the end, the background model samples and the factor parameters are differentiated updated according to the categories results. Using SkySat and Jilin No. 1 video data to do experiments. The experimental results show that our proposed method has favourable effect and performance, with the recall rate of results superior to 0.909. Comparing with the classical ViBe method,the error detection removal rate is superior to 90%.
引文
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[10]Zhang Jianqing,Duan Yan.A Supervised Classification Method of Polarimetric Synthetic Aperture Radar Data Using Watershed Segmentation and Decision Tree C5.0[J].Geomatics and Information Science of Wuhan University,2014,39(8):891-896(张剑清,段艳.一种结合分水岭与决策树C5.0的极化SAR分类方法[J].武汉大学学报·信息科学版,2014,39(8):891-896)
[11]Huang Yuda,Fan Taihua.ID3 Algorithm for Decision Tree Analysis and Optimization[J].Computer Engineering and Design,2012,33(8):3 089-3 093(黄宇达,范太华.决策树ID3算法的分析与优化[J].计算机工程与设计,2012,33(8):3 089-3 093)
[12]Wang Y,Jodoin P M,Porikli F,et al.CDnet2014:An Expanded Change Detection Benchmark Dataset[C].IEEE Conference on Computer Vision and Pattern Recognition Workshops,Columbus,USA,2014
[2]Wu Jiaqi,Zhang Guo,Wang Taoyang,et al.Satellite Video Point-Target Tracking in Combination with Motion Smoothness Constraint and Grayscale Feature[J].Acta Geodaetica et Cartographica Sinica,2017,46(9):1 135-1 146(吴佳奇,张过,汪韬阳,等.结合运动平滑约束与灰度特征的卫星视频点目标跟踪[J].测绘学报,2017,46(9):1 135-1 146)
[3]Liu Hui,Li Qingquan,Gao Chunxian,et al.Moving Target Detection Using C_SURF Registration[J].Geomatics and Information Science of Wuhan University,2014,39(8):951-955(刘慧,李清泉,高春仙,等.利用C_SURF配准的空基视频运动目标检测[J].武汉大学学报·信息科学版,2014,39(8):951-955)
[4]Wang Xia,Zhang Guo,Shen Xin,et al.Satellite Video Stabilization with Geometric Distortion[J].Acta Geodaetica et Cartographica Sinica,2016,45(2):194-198(王霞,张过,沈欣,等.顾及像面畸变的卫星视频稳像[J].测绘学报,2016,45(2):194-198)
[5]Yun K,Lim J,Jin Y C.Scene Conditional Background Update for Moving Object Detection in a Moving Camera[J].Pattern Recognition Letters,2017,88:57-63
[6]Min Weidong,Guo Xiaoguang,Han Qing.An Improved ViBe Algorithm and Its Application in Traffic Video Processing[J].Optics and Precision Engineering,2017,25(3):806-811(闵卫东,郭晓光,韩清.改进的ViBe算法及其在交通视频处理中的应用[J].光学精密工程,2017,25(3):806-811)
[7]Barnich O,Vanroogenbroeck M.ViBe:A Universal Background Subtraction Algorithm for Video Sequences[J].IEEE Transactions on Image Processing,2011,20(6):1 709-1 724
[8]He L,Chao Y,Suzuki K,et al.Fast ConnectedComponent Labeling[J].Pattern Recognition,2009,42(9):1 977-1 987
[9]Zhang Lin,Chen Yan,Li Taoying,et al.Research on Decision Tree Classification Algorithms[J].Computer Engineering,2011,37(13):66-67,70(张琳,陈燕,李桃迎,等.决策树分类算法研究[J].计算机工程,2011,37(13):66-67,70)
[10]Zhang Jianqing,Duan Yan.A Supervised Classification Method of Polarimetric Synthetic Aperture Radar Data Using Watershed Segmentation and Decision Tree C5.0[J].Geomatics and Information Science of Wuhan University,2014,39(8):891-896(张剑清,段艳.一种结合分水岭与决策树C5.0的极化SAR分类方法[J].武汉大学学报·信息科学版,2014,39(8):891-896)
[11]Huang Yuda,Fan Taihua.ID3 Algorithm for Decision Tree Analysis and Optimization[J].Computer Engineering and Design,2012,33(8):3 089-3 093(黄宇达,范太华.决策树ID3算法的分析与优化[J].计算机工程与设计,2012,33(8):3 089-3 093)
[12]Wang Y,Jodoin P M,Porikli F,et al.CDnet2014:An Expanded Change Detection Benchmark Dataset[C].IEEE Conference on Computer Vision and Pattern Recognition Workshops,Columbus,USA,2014
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