├── D11660006.pdf ├── Question1 ├── vibe_xiao_gui_ying │ ├── avi_to_image.m │ ├── hall │ │ ├── avi_to_image.m │ │ ├── result │ │ │ ├── 1.jpg │ │ │ ├── 17.jpg │ │ │ ├── 18.jpg │ │ │ ├── 19.jpg │ │ │ ├── 2.jpg │ │ │ ├── 20.jpg │ │ │ ├── 21.jpg │ │ │ ├── 22.jpg │ │ │ ├── 23.jpg │ │ │ ├── 24.jpg │ │ │ ├── 3.jpg │ │ │ ├── 4.jpg │ │ │ ├── 5.jpg │ │ │ ├── 6.jpg │ │ │ ├── 7.jpg │ │ │ ├── 8.jpg │ │ │ ├── Review.asv │ │ │ └── Review.m │ │ └── vibe_tom.m │ ├── office │ │ ├── avi_to_image.m │ │ ├── result │ │ │ ├── 22.jpg │ │ │ ├── 23.jpg │ │ │ ├── 24.jpg │ │ │ ├── 25.jpg │ │ │ ├── 26.jpg │ │ │ ├── 27.jpg │ │ │ ├── 28.jpg │ │ │ ├── 29.jpg │ │ │ ├── 41.jpg │ │ │ ├── 42.jpg │ │ │ ├── 43.jpg │ │ │ ├── 44.jpg │ │ │ ├── 45.jpg │ │ │ ├── 46.jpg │ │ │ ├── 47.jpg │ │ │ ├── 48.jpg │ │ │ └── Review.m │ │ ├── vibe_tom.m │ │ └── 第48帧mask.jpg │ ├── pedestrian │ │ ├── avi_to_image.m │ │ └── vibe_tom.m │ ├── smoke │ │ ├── avi_to_image.m │ │ └── vibe_tom.m │ ├── vibe_tom.asv │ ├── vibe_tom.m │ ├── 原图 │ │ ├── 1.jpg │ │ ├── 10.jpg │ │ ├── 11.jpg │ │ ├── 12.jpg │ │ ├── 13.jpg │ │ ├── 14.jpg │ │ ├── 15.jpg │ │ ├── 16.jpg │ │ ├── 17.jpg │ │ ├── 18.jpg │ │ ├── 19.jpg │ │ ├── 2.jpg │ │ ├── 20.jpg │ │ ├── 21.jpg │ │ ├── 22.jpg │ │ ├── 23.jpg │ │ ├── 24.jpg │ │ ├── 25.jpg │ │ ├── 26.jpg │ │ ├── 27.jpg │ │ ├── 28.jpg │ │ ├── 29.jpg │ │ ├── 3.jpg │ │ ├── 30.jpg │ │ ├── 31.jpg │ │ ├── 32.jpg │ │ ├── 33.jpg │ │ ├── 34.jpg │ │ ├── 35.jpg │ │ ├── 36.jpg │ │ ├── 37.jpg │ │ ├── 38.jpg │ │ ├── 39.jpg │ │ ├── 4.jpg │ │ ├── 40.jpg │ │ ├── 41.jpg │ │ ├── 42.jpg │ │ ├── 43.jpg │ │ ├── 44.jpg │ │ ├── 45.jpg │ │ ├── 46.jpg │ │ ├── 47.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ └── 第48帧原图像.jpg │ ├── 改进 │ │ ├── vibe_xiao_gui_ying309.jpg │ │ ├── vibe_xiao_gui_ying311.jpg │ │ ├── vibe_xiao_gui_ying312.jpg │ │ ├── vibe_xiao_gui_ying313.jpg │ │ ├── vibe_xiao_gui_ying314.jpg │ │ ├── vibe_xiao_gui_ying315.jpg │ │ ├── vibe_xiao_gui_ying316.jpg │ │ ├── vibe_xiao_gui_ying317.jpg │ │ └── 第48帧改进的Vibe.jpg │ └── 第48帧未改进Vibe.jpg └── 混合高斯 效果不太好 │ ├── 32.jpg │ ├── 33.jpg │ ├── 34.jpg │ ├── 35.jpg │ ├── 36.jpg │ ├── 37.jpg │ ├── 38.jpg │ ├── 39.jpg │ ├── 40.jpg │ ├── 41.jpg │ ├── 42.jpg │ ├── 43.jpg │ ├── 44.jpg │ ├── 45.jpg │ ├── 46.jpg │ ├── GaoSi.asv │ ├── GaoSi.m │ ├── avi_to_image.m │ └── 静态背景单帧 │ ├── 1.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ └── 9.jpg ├── Question2 └── 动态背景 │ ├── 1.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ ├── 9.jpg │ ├── aaa.avi │ ├── avi_to_image.m │ ├── gaosi │ ├── GaoSi.asv │ ├── GaoSi.m │ ├── gaosi302.jpg │ ├── gaosi303.jpg │ ├── gaosi304.jpg │ ├── gaosi305.jpg │ ├── gaosi306.jpg │ ├── gaosi307.jpg │ ├── gaosi308.jpg │ ├── gaosi309.jpg │ ├── gaosi310.jpg │ ├── gaosi311.jpg │ ├── gaosi312.jpg │ ├── gaosi313.jpg │ ├── gaosi314.jpg │ ├── gaosi315.jpg │ └── gaosi316.jpg │ ├── img2avi.asv │ ├── img2avi.m │ ├── vibe_tom.m │ ├── waterSurface │ ├── avi_to_image.m │ ├── result │ │ ├── 1.jpg │ │ ├── 10.jpg │ │ ├── 11.jpg │ │ ├── 12.jpg │ │ ├── 13.jpg │ │ ├── 14.jpg │ │ ├── 15.jpg │ │ ├── 16.jpg │ │ ├── 17.jpg │ │ ├── 18.jpg │ │ ├── 19.jpg │ │ ├── 2.jpg │ │ ├── 20.jpg │ │ ├── 21.jpg │ │ ├── 22.jpg │ │ ├── 23.jpg │ │ ├── 24.jpg │ │ ├── 25.jpg │ │ ├── 26.jpg │ │ ├── 27.jpg │ │ ├── 28.jpg │ │ ├── 29.jpg │ │ ├── 3.jpg │ │ ├── 30.jpg │ │ ├── 39.jpg │ │ ├── 4.jpg │ │ ├── 40.jpg │ │ ├── 41.jpg │ │ ├── 42.jpg │ │ ├── 43.jpg │ │ ├── 44.jpg │ │ ├── 45.jpg │ │ ├── 46.jpg │ │ ├── 47.jpg │ │ ├── 48.jpg │ │ ├── 5.jpg │ │ ├── 6.jpg │ │ ├── 7.jpg │ │ ├── 8.jpg │ │ ├── 9.jpg │ │ └── Review.m │ └── vibe_tom.m │ ├── 单帧 │ ├── 18.jpg │ ├── 19.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ ├── 30.jpg │ ├── 31.jpg │ ├── 32.jpg │ ├── 33.jpg │ ├── 34.jpg │ ├── 35.jpg │ ├── 36.jpg │ ├── 37.jpg │ ├── 38.jpg │ ├── 39.jpg │ ├── 40.jpg │ ├── 41.jpg │ ├── 42.jpg │ ├── 43.jpg │ ├── 44.jpg │ ├── 45.jpg │ ├── 46.jpg │ ├── 47.jpg │ ├── 48.jpg │ └── GaoSi.m │ ├── 未改 │ ├── 1.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ ├── 3.jpg │ ├── 30.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ └── 9.jpg │ ├── 第30帧Vibe改进算法.jpg │ └── 结果 │ ├── vibe_xiao_gui_ying289.jpg │ ├── vibe_xiao_gui_ying290.jpg │ ├── vibe_xiao_gui_ying291.jpg │ ├── vibe_xiao_gui_ying292.jpg │ ├── vibe_xiao_gui_ying293.jpg │ ├── vibe_xiao_gui_ying294.jpg │ ├── vibe_xiao_gui_ying295.jpg │ ├── vibe_xiao_gui_ying296.jpg │ ├── vibe_xiao_gui_ying297.jpg │ ├── vibe_xiao_gui_ying298.jpg │ ├── vibe_xiao_gui_ying299.jpg │ ├── vibe_xiao_gui_ying300.jpg │ ├── vibe_xiao_gui_ying301.jpg │ ├── vibe_xiao_gui_ying302.jpg │ ├── vibe_xiao_gui_ying303.jpg │ ├── vibe_xiao_gui_ying304.jpg │ ├── vibe_xiao_gui_ying305.jpg │ ├── vibe_xiao_gui_ying306.jpg │ ├── vibe_xiao_gui_ying307.jpg │ ├── vibe_xiao_gui_ying308.jpg │ ├── vibe_xiao_gui_ying309.jpg │ ├── vibe_xiao_gui_ying310.jpg │ ├── vibe_xiao_gui_ying311.jpg │ ├── vibe_xiao_gui_ying312.jpg │ ├── vibe_xiao_gui_ying313.jpg │ ├── vibe_xiao_gui_ying314.jpg │ ├── vibe_xiao_gui_ying315.jpg │ ├── vibe_xiao_gui_ying316.jpg │ ├── vibe_xiao_gui_ying317.jpg │ └── vibe_xiao_gui_ying318.jpg ├── Question3 ├── GPA │ ├── avi_to_image.m │ ├── input.m │ └── myGPA.m ├── GaoSi.m ├── avi_to_image.m ├── vibe_tom.asv ├── vibe_tom.m ├── 单帧1 │ ├── 1.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ ├── 3.jpg │ ├── 30.jpg │ ├── 31.jpg │ ├── 32.jpg │ ├── 33.jpg │ ├── 34.jpg │ ├── 35.jpg │ ├── 36.jpg │ ├── 37.jpg │ ├── 38.jpg │ ├── 39.jpg │ ├── 4.jpg │ ├── 40.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ ├── 9.jpg │ ├── GaoSi.m │ └── 结果 │ │ ├── 10.jpg │ │ ├── 11.jpg │ │ ├── 12.jpg │ │ ├── 13.jpg │ │ ├── 14.jpg │ │ ├── 15.jpg │ │ ├── 16.jpg │ │ ├── 17.jpg │ │ ├── 18.jpg │ │ ├── 19.jpg │ │ ├── 20.jpg │ │ ├── 21.jpg │ │ ├── 22.jpg │ │ ├── 23.jpg │ │ ├── 24.jpg │ │ ├── 25.jpg │ │ ├── 26.jpg │ │ ├── 27.jpg │ │ ├── 28.jpg │ │ ├── 29.jpg │ │ ├── 30.jpg │ │ ├── 31.jpg │ │ ├── 32.jpg │ │ ├── 33.jpg │ │ ├── 34.jpg │ │ ├── 35.jpg │ │ ├── 36.jpg │ │ ├── 37.jpg │ │ ├── 8.jpg │ │ └── 9.jpg ├── 单帧2 │ ├── 1.jpg │ ├── 10.jpg │ ├── 11.jpg │ ├── 12.jpg │ ├── 13.jpg │ ├── 14.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 3.jpg │ ├── 4.jpg │ ├── 5.jpg │ ├── 6.jpg │ ├── 7.jpg │ ├── 8.jpg │ ├── 9.jpg │ └── GaoSi.m └── 未调试出stabilize-image │ ├── K │ └── BackgKalman_m.m │ ├── getGrayCodeBitPlane.m │ ├── imageStabilizeMain.m │ ├── motion │ └── motioncompensation.m │ └── stabilizeMovie_GCBPM.m └── README.md /D11660006.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MichaelBeechan/ImageProcessing_Mathematical_Modeling/9be05e057e8c36d69b169d1c384bedc0214cac2a/D11660006.pdf -------------------------------------------------------------------------------- /Question1/vibe_xiao_gui_ying/avi_to_image.m: 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In = size(T); % 3D tensor 6 | %% Estimate affine global motion with 6?parameters 7 | iters = [5,3,3]; 8 | M = zeros(3,3,In(3)); 9 | for k = 1:In (3) 10 | M(:,:,k) = estMotionMulti2(T(:,:,1),T(:,:,k),iters ,[],1,1); 11 | end 12 | %% Inverse affine transform and build new tensor 13 | Tnew = T; 14 | for k = 2:In (3) 15 | Tnew(:,:,k) = warpAffine2(T(:,:,k),M(:,:,k)); 16 | end 17 | %% Process all sub?blocks 18 | sT = size(T);blksz = 8; 19 | d = zeros(sT(end),prod(sT (1:2))/ blksz ^2); 20 | kblk = 0; thresh = 0.005; 21 | xy = zeros(1,2); 22 | Imbgr = zeros(sT(1:2)); 23 | for xu = 1:8: sT(1)-7 24 | for yu = 1:8: sT(2)-7 25 | kblk = kblk + 1; 26 | Tblk = Tnew(xu:xu+blksz-1, yu:yu+blksz-1,:); 27 | nanind = find(isnan(Tblk)); 28 | [r,c,nanfr] = ind2sub(size(Tblk),nanind); 29 | Tblknew = Tblk(:,:,setdiff (1:sT(end),unique(nanfr ))); 30 | %% Factorize subtensor with Parafac algorithms R = 1 31 | Yblk = permute(tensor(Tblknew),[3 1 2 ]); 32 | A_= cp_als(Yblk ,1); 33 | d = full( A_{1}); 34 | 35 | edges = [min(d): thresh:max(d) max(d)+eps] ; 36 | [n,bin] = histc(d ,edges); 37 | m = mode(bin); 38 | indbgr = find ((d >= edges(m)) & (d <= edges(m+1))); 39 | Imbgr(xu:xu+blksz-1,yu:yu+blksz-1)=median(Tblknew(:,:,indbgr),3); 40 | end 41 | end 42 | %% Display the estimated background image 43 | imshow(uint8(Imbgr)) -------------------------------------------------------------------------------- /Question3/未调试出stabilize-image/stabilizeMovie_GCBPM.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/MichaelBeechan/ImageProcessing_Mathematical_Modeling/9be05e057e8c36d69b169d1c384bedc0214cac2a/Question3/未调试出stabilize-image/stabilizeMovie_GCBPM.m -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ImageProcessing_Mathematical_Modeling 2 | 3 | ##################################################################### 4 | ###################################持 续 更 新######################## 5 | ##################################################################### 6 | 7 | Other data you can find in:https://download.csdn.net/download/u011344545/10442630 and 8 | https://download.csdn.net/download/u011344545/10446455 9 | 10 | “华为杯”第十四届中国研究生数学建模竞赛（图像处理篇）——获全国二等奖 11 | 12 | 《基于监控视频的前景目标提取》 13 | 14 | 在中国安防产业中视频监控作为最重要的信息获取手段之一，能对目标有效的提取是重要而基础的问题，因此本文在此背景下，围绕对监控视频的前景目标有效的提取问题，研究了关于 1）静态背景、动态背景的前景目标提取，能在背景复杂化的条件下，将运动的目标；2）带抖动视频；3）静态背景下多摄像头对多目标提取；4）出现异常事件视频的判断等问题。给出了在不同情况下的前景目标提取方案。 15 | 问题一是针对静态背景且摄像头稳定的情况下，如何对前景目标提取的问题。在题目要求的基础上，通过对附件 2 中几组视频的分析，我们发现所有前景目标的运动短暂且光线明暗变化不明显。由于传统的 Vibe 算法能抑制鬼影但是运行效果不理想，因此采用建立在帧差法上改进的 Vibe 算法模型求解问题。并和传统的 Vibe 算法做对比，结果显示改进的 Vibe 算法明显优于传统的算法。而且对我们的算法模型做了效果评价。详细数据参考正文与附录。 16 | 问题二是在背景为动态（如有水波的产生）的情况下，对前景目标的提取问题。在此问题中，由于动态背景存在使得提取出的图像帧具有大量的干扰噪声，对前景目标的识别和提取造成干扰，因此我们提出一种基于全局外观一致型的运动目标检测法。在用 Vibe 算法对场景预检测的基础上，建立混合高斯模型分别对前景和背景进行全局外观建模，将运动目标检测出来，再引入超像素去噪，进一步优化结果。详细结果参考正文与附录。 17 | 问题三是在问题一、二基础上的进一步深化。问题一及问题二是建立在摄像机自身稳定的基础上，而问题三则是在摄像机抖动的情况下。由于摄像机抖动一般具有旋转和平移，因此我们建立了坐标变换模型，以仿射变换作为模型基础，结合改进的高精度鲁棒的 RANSAC 算法提取前景目标，并对比灰度投影法，比较两种模型效果。具体效果见正文与附录。 18 | 问题四是对前三个问题的综合应用。运用基于混合高斯模型背景建模 Vibe算法，对前景目标进行提取；选出具有显著前景目标的参考帧，计算参考帧中显著前景目标所占的面积，并将此面积设定为阈值 T，遍历所有的视频帧，计算其前景目标所占的面积，通过相减对比，判定显著前景目标。若判定为显著前景目标则输出其所在视频帧中的帧号，并将显著前景出现的总帧数增加 1。 19 | 问题五是针对多摄像头多目标的协同跟踪问题。在问题二的混合高斯模型基础上我们建立了动态背景提取法，对不断变化的背景进行实时更新。再利用单应性约束法对多目标发生重叠现象进行投影将重叠目标区分开来，对目标进行定位。由于目标的不断运动，我们采用粒子滤波法对前景目标进行实时跟踪，通过多摄像头的协同通信完成对多前景目标的检测。 20 | 问题六是针对监控视频中前景目标出现异常情况时判断是否有异常事件的问题。在基于稀疏表示的模型上，引入混合高斯模型用于学习不同类型的运动特征规律，然后通过各个单高斯模型中的均值建立一个相似矩阵作为字典。以测试阶段生成的核矢量为基础，用该局部特征的核矢量计算基于稀疏表示的重构误差，并将其与已设定的阈值进行比较，如果重构误差大于阈值，则判为异常。 21 | 22 | 关键词：监控视频，前景目标提取，Vibe 算法，混合高斯模型，目标跟踪 23 | --------------------------------------------------------------------------------