结合运动平滑约束与灰度特征的卫星视频点目标跟踪
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摘要
针对卫星视频条件下的点目标跟踪问题,提出了一种运动平滑约束的贝叶斯分类目标跟踪方法(BMoST)。本方法引入朴素贝叶斯分类器的思想,不依赖目标的任何先验概率,在运动平滑性约束下,利用灰度相似性特征来表达描述目标的似然度,并根据独立假设的贝叶斯定理,建立简化的分类器条件概率修正模型,通过该模型估计目标的后验概率,从而实现目标跟踪。同时,采用卡尔曼滤波辅助、优化跟踪处理,提高算法的稳健性。试验数据采用SkySat和吉林一号拍摄的视频各两段,对6个点目标进行跟踪试验。结果表明,本文提出的方法针对卫星视频的点目标跟踪效果良好,精度达到90%左右,且跟踪轨迹平滑,满足卫星视频后续高级处理和应用需要。
In view of the problem of satellite video point-target tracking,a method of Bayesian classification for tracking with the constraint of motion smoothness is proposed,which named Bayesian MoST.The idea of naive Bayesian classification without relying on any prior probability of target is introduced.Under the constraint of motion smoothness,the gray level similarity feature is used to describe the likelihood of the target.And then,the simplified conditional probability correction model of classifier is created according to the independence assumption Bayes theorem.Afterwards,the tracking target position can be determined by estimating the target posterior probability on the basis of the model.Meanwhile,the Kalman filter,an assistance and optimization method,is used to enhance the robustness of tracking processing.The theoretical method proposed are validated in a number of six experiments using SkySat and JL1 Hvideo,each has two segments.The experiment results show that the BMoST method proposed have good performance,the tracking precision is about 90%and tracking trajectory is smoothing.The method could satisfy the needs of the following advanced treatment in satellite video.
In view of the problem of satellite video point-target tracking,a method of Bayesian classification for tracking with the constraint of motion smoothness is proposed,which named Bayesian MoST.The idea of naive Bayesian classification without relying on any prior probability of target is introduced.Under the constraint of motion smoothness,the gray level similarity feature is used to describe the likelihood of the target.And then,the simplified conditional probability correction model of classifier is created according to the independence assumption Bayes theorem.Afterwards,the tracking target position can be determined by estimating the target posterior probability on the basis of the model.Meanwhile,the Kalman filter,an assistance and optimization method,is used to enhance the robustness of tracking processing.The theoretical method proposed are validated in a number of six experiments using SkySat and JL1 Hvideo,each has two segments.The experiment results show that the BMoST method proposed have good performance,the tracking precision is about 90%and tracking trajectory is smoothing.The method could satisfy the needs of the following advanced treatment in satellite video.
引文
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[19]CHEN Z W,CHIANG C C,HSIEH Z T.Extending 3D Lucas-Kanade Tracking with Adaptive Templates for Head Pose Estimation[J].Machine Vision and Applications,2010,21(6):889-903.
[20]FORTUN D,BOUTHEMY P,KERVRANN C.Optical Flow Modeling and Computation:A Survey[J].Computer Vision and Image Understanding,2015,134:1-21.
[21]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.DOI:10.11947/j.AGCS.2016.20150238.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.DOI:10.11947/j.AGCS.2016.20150238.
[22]高广珠,李忠武,余理富,等.归一化互相关系数在图像序列目标检测中的应用[J].计算机工程与科学,2005,27(3):38-40.GAO Guangzhu,LI Zhongwu,YU Lifu,et al.Application of the Normalized Cross Correlation Coefficient in Image Sequence Object Detection[J].Computer Engineering&Science,2005,27(3):38-40.
[23]吕昊,林君,曾晓献.改进朴素贝叶斯分类算法的研究与应用[J].湖南大学学报(自然科学版),2012,39(12):56-61.LHao,LIN Jun,ZENG Xiaoxian.Research and Application of Improved Naive Bayesian Classification Algorithm[J].Journal of Hunan University(Natural Sciences),2012,39(12):56-61.
[24]ZHANG Kaihua,ZHANG Lei,YANG Minghsuan.Realtime Compressive Tracking[C]∥European Conference on Computer Vision.Berlin,Heidelberg:Springer,2012:864-877.
[25]耿磊,王学彬,肖志涛,等.结合特征筛选与二次定位的快速压缩跟踪算法[J].自动化学报,2016,42(9):1421-1431.GENG Lei,WANG Xue-bin,XIAO Zhitao,et al.Fast Compressive Tracking Algorithm Combining Feature Selection with Secondary Localization[J].Acta Automatica Sinica,2016,42(9):1421-1431.
[26]HU Ming-kuei.Visual Pattern Recognition by Moment Invariants[J].IRE Transactions on Information Theory,1962,8(2):179-187.
[27]黄敏,马亚琼,朱颢东,等.基于Hu修正不变矩的商标识别[J].华中师范大学学报(自然科学版),2014,48(2):182-185.HUANG Min,MA Yaqiong,ZHU Haodong,et al.Trademark Recognition Based on Hu Modified Invariant Moment[J].Journal of Huazhong Normal University(Natural Sciences),2014,48(2):182-185.
[28]闫钧华,陈少华,艾淑芳,等.基于Kalman预测器的改进的CAMShift目标跟踪[J].中国惯性技术学报,2014,22(4):536-542.YAN Junhua,CHEN Shaohua,AI Shufang,et al.Target Tracking with Improved CAMShift Based on Kalman Predictor[J].Journal of Chinese Inertial Technology,2014,22(4):536-542.
[2]张过.卫星视频处理与应用进展[J].应用科学学报,2016,34(4):361-370.ZHANG Guo.Satellite Video Processing and Applications[J].Journal of Applied Sciences-Electronics and Information Engineering,2016,34(4):361-370.
[3]朱厉洪,回征,任德锋,等.视频成像卫星发展现状与启示[J].卫星应用,2015(10):23-28.ZHU Lihong,HUI Zheng,REN Defeng,et al.Development Status and Inspiration of Video Imaging Satellite[J].Satellite Application,2015(10):20-28.
[4]D’ANGELO P,MáTTYUS G,REINARTZ P.Skybox Image and Video Product Evaluation[J].International Journal of Image and Data Fusion,2016,7(1):3-8.
[5]王飞,房胜.加权局部特征结合判别式字典的目标跟踪[J].中国图象图形学报,2014,19(9):1316-1323.WANG Fei,FANG Sheng.Visual Tracking Based on the Discriminative Dictionary and Weighted Local Features[J].Journal of Image and Graphics,2014,19(9):1316-1323.
[6]LEICHTER I.Mean Shift Trackers with Cross-bin Metrics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(4):695-706.
[7]ZhANG Dongmei,WANG Ronggui,YANG Juan,et al.Research on Sparse Coding Object Tracking Method[J].Computer Engineering and Applications,2015,51(22):53-60.
[8]HENRIQUES J F,CASEIRO R,MARTINS P,et al.Highspeed Tracking with Kernelized Correlation Filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):583-596.
[9]ZDENEK K,MIKOLAJCZYK K,MATAS J.Trackinglearning-detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1409-1422.
[10]周志宇,彭小龙,吴迪冲,等.在线特征选取的多示例学习目标跟踪[J].中国图象图形学报,2015,20(10):1346-1357.ZHOU Zhiyu,PENG Xiaolong,WU Dichong,et al.Object Tracking Based on Multiple Instance Learning with Online Feature Selection[J].Journal of Image and Graphics,2015,20(10):1346-1357.
[11]朱秋平,颜佳,张虎,等.基于压缩感知的多特征实时跟踪[J].光学精密工程,2013,21(2):437-444.ZHU Qiuping,YAN Jia,ZHANG Hu,et al.Real-time Tracking Using Multiple Features Based on Compressive Sensing[J].Optics and Precision Engineering,2013,21(2):437-444.
[12]XU Jianqiang,LU Yao,LIU Jinwu.Robust Tracking via Weighted Spatio-temporal Context Learning[C]∥Proceedings of 2014IEEE International Conference on Image Processing.Paris,France:IEEE,2014:413-416.
[13]ZAPPELLA L,DEL BUE A,LLADO X,et al.Joint Estimation of Segmentation and Structure from Motion[J].Computer Vision and Image Understanding,2013,117(2):113-129.
[14]王春平,王暐,刘江义,等.基于色度饱和度-角度梯度直方图特征的尺度自适应核相关滤波跟踪[J].光学精密工程,2016,24(9):2293-2301.WANG Chunping,WANG Wei,LIU Jiangyi,et al.Scale Adaptive Kernelized Correlation Filter Tracking Based on HHS-OG Feature[J].Optics and Precision Engineering,2016,24(9):2293-2301.
[15]修春波,魏世安,万蓉凤.二维联合特征模型的自适应均值漂移目标跟踪[J].光电子·激光,2015(2):342-351.XIU Chunbo,WEI Shian,WAN Rongfeng.CAMShift Target Tracking Based on Two-dimensional Joint Characteristics[J].Journal of Optoelectronics·Laser,2015(2):342-351.
[16]BERTINETTO L,VALMADRE J,GOLODETZ S,et al.Staple:Complementary Learners for Real-time Tracking[C]∥Proceedings of 2016IEEE International Conference on Computer Vision and Pattern Recognition.Las Vegas,NV:IEEE,2016:1401-1409.
[17]ZHANG Yanning,TONG Xiaomin,YANG Tao,et al.Multi-model Estimation Based Moving Object Detection for Aerial Video[J].Sensors(Basel,Switzerland),2015,15(4):8214-8231.
[18]SHI Jianbo,TOMASI C.Good Features to Track[C]∥Proceedings of 1994IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Seattle,WA:IEEE,1994:593-600.
[19]CHEN Z W,CHIANG C C,HSIEH Z T.Extending 3D Lucas-Kanade Tracking with Adaptive Templates for Head Pose Estimation[J].Machine Vision and Applications,2010,21(6):889-903.
[20]FORTUN D,BOUTHEMY P,KERVRANN C.Optical Flow Modeling and Computation:A Survey[J].Computer Vision and Image Understanding,2015,134:1-21.
[21]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.DOI:10.11947/j.AGCS.2016.20150238.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.DOI:10.11947/j.AGCS.2016.20150238.
[22]高广珠,李忠武,余理富,等.归一化互相关系数在图像序列目标检测中的应用[J].计算机工程与科学,2005,27(3):38-40.GAO Guangzhu,LI Zhongwu,YU Lifu,et al.Application of the Normalized Cross Correlation Coefficient in Image Sequence Object Detection[J].Computer Engineering&Science,2005,27(3):38-40.
[23]吕昊,林君,曾晓献.改进朴素贝叶斯分类算法的研究与应用[J].湖南大学学报(自然科学版),2012,39(12):56-61.LHao,LIN Jun,ZENG Xiaoxian.Research and Application of Improved Naive Bayesian Classification Algorithm[J].Journal of Hunan University(Natural Sciences),2012,39(12):56-61.
[24]ZHANG Kaihua,ZHANG Lei,YANG Minghsuan.Realtime Compressive Tracking[C]∥European Conference on Computer Vision.Berlin,Heidelberg:Springer,2012:864-877.
[25]耿磊,王学彬,肖志涛,等.结合特征筛选与二次定位的快速压缩跟踪算法[J].自动化学报,2016,42(9):1421-1431.GENG Lei,WANG Xue-bin,XIAO Zhitao,et al.Fast Compressive Tracking Algorithm Combining Feature Selection with Secondary Localization[J].Acta Automatica Sinica,2016,42(9):1421-1431.
[26]HU Ming-kuei.Visual Pattern Recognition by Moment Invariants[J].IRE Transactions on Information Theory,1962,8(2):179-187.
[27]黄敏,马亚琼,朱颢东,等.基于Hu修正不变矩的商标识别[J].华中师范大学学报(自然科学版),2014,48(2):182-185.HUANG Min,MA Yaqiong,ZHU Haodong,et al.Trademark Recognition Based on Hu Modified Invariant Moment[J].Journal of Huazhong Normal University(Natural Sciences),2014,48(2):182-185.
[28]闫钧华,陈少华,艾淑芳,等.基于Kalman预测器的改进的CAMShift目标跟踪[J].中国惯性技术学报,2014,22(4):536-542.YAN Junhua,CHEN Shaohua,AI Shufang,et al.Target Tracking with Improved CAMShift Based on Kalman Predictor[J].Journal of Chinese Inertial Technology,2014,22(4):536-542.