1.软件版本

matlab2013b

2.系统概述

3.部分源码

clc;
clear;
close all;
warning off;
addpath 'func\'%训练数据
Is = imread('Track_file\tower1.bmp');
load Track_file\track1.mat
load Track_file\track2.matfigure(1);
subplot(121);
imshow(Is);
hold on;
X1=[];
Y1=[];
T1=[];
W1=[];
H1=[];
for i = 1:length(track1)iif isempty(track1(1,i).x) == 1X = 0;Y = 0;elseX = cell2mat(track1(1,i).x(1,1));Y = cell2mat(track1(1,i).y(1,1));X1=[X1;cell2mat(track1(1,i).x(1,1))];Y1=[Y1;cell2mat(track1(1,i).y(1,1))];T1=[T1;cell2mat(track1(1,i).time(1,1))];W1=[W1;cell2mat(track1(1,i).w(1,1))];H1=[H1;cell2mat(track1(1,i).h(1,1))];        endplot(X,Y,'c.');hold on;
end
title('Track1');subplot(122);
imshow(Is);
hold on;
X2=[];
Y2=[];
T2=[];
W2=[];
H2=[];
for i = 1:length(track2)iif isempty(track2(1,i).x) == 1X = 0;Y = 0;elseX = cell2mat(track2(1,i).x(1,1));Y = cell2mat(track2(1,i).y(1,1));X2=[X2;cell2mat(track2(1,i).x(1,1))];Y2=[Y2;cell2mat(track2(1,i).y(1,1))];T2=[T2;cell2mat(track2(1,i).time(1,1))];W2=[W2;cell2mat(track2(1,i).w(1,1))];H2=[H2;cell2mat(track2(1,i).h(1,1))];  endplot(X,Y,'c.');hold on;
end
title('Track2');tao=20;
X1s=[];
Y1s=[];
T1s=[];
W1s=[];
H1s=[];
X_samp  = [];
Y_samp  = [];
T_samp  = [];
W_samp  = [];
H_samp  = [];
X_samps1 = [];
Y_samps1 = [];
T_samps1 = [];
W_samps1 = [];
H_samps1 = [];
for i = 1:length(track1)iif isempty(track1(1,i).x) == 1X1s{i}=0;Y1s{i}=0;T1s{i}=0;W1s{i}=0;H1s{i}=0;elseX1s{i}=cell2mat(track1(1,i).x(1,1));Y1s{i}=cell2mat(track1(1,i).y(1,1));T1s{i}=cell2mat(track1(1,i).time(1,1));W1s{i}=cell2mat(track1(1,i).w(1,1));H1s{i}=cell2mat(track1(1,i).h(1,1));endif i <= tao;X_samp = (X1s{i});Y_samp = (Y1s{i});T_samp = 0.00001*ones(length(X_samp),1);W_samp = (W1s{i});H_samp = (H1s{i});elseX_samp = (X1s{i});Y_samp = (Y1s{i});T_samp = 0.00001*ones(length(X_samp),1);Ls           = min(length(X1s{i}),length(X1s{i-tao}));for j = 1:LsT_samp(j,1) = sqrt((X1s{i}(j) - X1s{i-tao}(j))^2 + (Y1s{i}(j) - Y1s{i-tao}(j))^2)/tao;endW_samp = (W1s{i});H_samp = (H1s{i});endX_samps1 = [X_samps1;X_samp];Y_samps1 = [Y_samps1;Y_samp];T_samps1 = [T_samps1;T_samp];W_samps1 = [W_samps1;W_samp];H_samps1 = [H_samps1;H_samp];
endX2s=[];
Y2s=[];
T2s=[];
W2s=[];
H2s=[];
X_samp  = [];
Y_samp  = [];
T_samp  = [];
W_samp  = [];
H_samp  = [];
X_samps2 = [];
Y_samps2 = [];
T_samps2 = [];
W_samps2 = [];
H_samps2 = [];
for i = 1:length(track2)iif isempty(track2(1,i).x) == 1X2s{i}=0;Y2s{i}=0;T2s{i}=0;W2s{i}=0;H2s{i}=0;elseX2s{i}=cell2mat(track2(1,i).x(1,1));Y2s{i}=cell2mat(track2(1,i).y(1,1));T2s{i}=cell2mat(track2(1,i).time(1,1));W2s{i}=cell2mat(track2(1,i).w(1,1));H2s{i}=cell2mat(track2(1,i).h(1,1));endif i <= tao;X_samp = (X2s{i});Y_samp = (Y2s{i});T_samp = 0.00001*ones(length(X_samp),1);W_samp = (W2s{i});H_samp = (H2s{i});elseX_samp = (X2s{i});Y_samp = (Y2s{i});T_samp = 0.00001*ones(length(X_samp),1);Ls           = min(length(X2s{i}),length(X2s{i-tao}));for j = 1:LsT_samp(j,1) = sqrt((X2s{i}(j) - X2s{i-tao}(j))^2 + (Y2s{i}(j) - Y2s{i-tao}(j))^2)/tao;endW_samp = (W2s{i});H_samp = (H2s{i});endX_samps2 = [X_samps2;X_samp];Y_samps2 = [Y_samps2;Y_samp];T_samps2 = [T_samps2;T_samp];W_samps2 = [W_samps2;W_samp];H_samps2 = [H_samps2;H_samp];
end%原始数据的训练,用set2的数据来训练
feature1    = [X_samps1,Y_samps1,T_samps1,W_samps1,H_samps1];
feature2    = [X_samps2,Y_samps2,T_samps2,W_samps2,H_samps2];
feature     = [feature2(5000:7000,:)];%
tmps        = feature;
[Px, model] = gmm(tmps,2);
% save EmGmm_model.mat model% figure;
% imshow(Is);
% hold on;
% [~, cls_ind] = max(Px',[],1);
% for tt = 1:1
%     tt
%     for jj = 1:length(cls_ind);
%         if cls_ind(jj)==1
%            plot(feature(jj,1),feature(jj,2),'b.');
%         end
%         if cls_ind(jj)==2
%            plot(feature(jj,1),feature(jj,2),'r.');
%         end
%         hold on;
%     end
%     title('异常行为识别');
% end

clc;
clear;
close all;
warning off;
addpath 'func\'%初始背景的提取
%注意，原始的视频非常大，这里需要截取其中需要处理的帧数
Bg_Length   = 20;
Start_Frame = Bg_Length+1;
End_Frame   = 999;
Simulation_frame = min(800,End_Frame);
avi1        = aviread('vedio\4.AVI',[Start_Frame:End_Frame]);
vedio1      = {avi1.cdata};
avi2        = aviread('vedio\4.AVI',[1:Bg_Length]);
vedio2      = {avi2.cdata};
vedio       = [vedio2,vedio1];
%步骤一:结合高斯混合模型的帧差法进行目标的提取
%这个步骤主要是目标提取，并获得特征序列两个部分
RR                  = 240;%处理视频大小
CC                  = 320;
K                   = 4;                   %组件
Alpha               = 0.002;               %适应权重速度
Rho                 = 0.01;                %适应权重速度协方差
Deviation_sq        = 49;                  %阈值用于查找匹配
Variance            = 2;                   %初始方差为新放置组件
Props               = 0.00001;             %最初为新放置
Back_Thresh         = 0.6;                 %体重的比例必须占背景模型
Comp_Thresh         = 10;                  %滤掉连接组件的较小的尺寸
SHADOWS             = [0.7,0.25,0.85,0.95];
frameNum_Original   = length(vedio);CRGB = 3;
D    = RR * CC;
Temp = zeros(RR,CC,CRGB,frameNum_Original,'uint8');for tt = 1:frameNum_Originalim                       = vedio{tt};Temp(:,:,:,tt)           = im;
end
Temp = reshape(Temp,size(Temp,1)*size(Temp,2),size(Temp,3),size(Temp,4));      Mus                 = zeros(D,K,CRGB);
Mus(:,1,:)          = double(Temp(:,:,1));
Mus(:,2:K,:)        = 255*rand([D,K-1,CRGB]);
Sigmas              = Variance*ones(D,K,CRGB);
Weights             = [ones(D,1),zeros(D,K-1)];
Squared             = zeros(D,K);
Gaussian            = zeros(D,K);
Weight              = zeros(D,K);
background          = zeros(RR,CC);
Shadows             = zeros(RR,CC);
Images0             = zeros(RR,CC);
Images1             = zeros(RR,CC);
Images2             = zeros(RR,CC);
background_Update   = zeros(RR,CC,CRGB); Xft        = [];
Yft        = [];
Timeft     = [];
Wft        = [];
Hft        = [];
Smin       = 50;
Smax       = 150;
PAlpha_min = 0.001;
PAlpha_max = 0.004;
Pdf1       = 0;
Pdf2       = 0;
tao        = 20;
Xx         = [];
flag       = 0;
Xf_save    = cell(End_Frame,1);
Yf_save    = cell(End_Frame,1);
Timef_save = cell(End_Frame,1);
Wf_save    = cell(End_Frame,1);
Hf_save    = cell(End_Frame,1);
S          = 0;
load EmGmm_model.mat;for tt = 1:Simulation_framedisp('当前帧数');tt%由分析得到的反馈信号%由分析得到的反馈信号%Minimum Object Size 反馈，目标大小检测if tt > 1Ss = Smin*Pdf1 + Smax*(1-Pdf1);elseSs = Smin; end%Background Learning Rate 反馈,背景更新学习率if tt > 1Alpha = PAlpha_min*Pdf2 + PAlpha_max*(1-Pdf2);elseAlpha = PAlpha_min; end%基于高斯混合模型的帧差分背景提取算法%基于高斯混合模型的帧差分背景提取算法image = Temp(:,:,tt);for kk = 1:K   Datac         = double(Temp(:,:,tt))-reshape(Mus(:,kk,:),D,CRGB);Squared(:,kk) = sum((Datac.^ 2)./reshape(Sigmas(:,kk,:),D,CRGB),2); end[junk,index] = min(Squared,[],2); Gaussian                                                = zeros(size(Squared));Gaussian(sub2ind(size(Squared),1:length(index),index')) = ones(D,1);Gaussian                                                = Gaussian&(Squared<Deviation_sq);%参数更新Weights = (1-Alpha).*Weights+Alpha.*Gaussian;for kk = 1:Kpixel_matched   = repmat(Gaussian(:,kk),1,CRGB);pixel_unmatched = abs(pixel_matched-1);Mu_kk           = reshape(Mus(:,kk,:),D,CRGB);Sigma_kk        = reshape(Sigmas(:,kk,:),D,CRGB);Mus(:,kk,:)     = pixel_unmatched.*Mu_kk+pixel_matched.*(((1-Rho).*Mu_kk)+(Rho.*double(image)));Mu_kk           = reshape(Mus(:,kk,:),D,CRGB); Sigmas(:,kk,:)  = pixel_unmatched.*Sigma_kk+pixel_matched.*(((1-Rho).*Sigma_kk)+repmat((Rho.* sum((double(image)-Mu_kk).^2,2)),1,CRGB));       endreplaced_gaussian   = zeros(D,K); mismatched          = find(sum(Gaussian,2)==0);       for ii = 1:length(mismatched)[junk,index]                            = min(Weights(mismatched(ii),:)./sqrt(Sigmas(mismatched(ii),:,1)));replaced_gaussian(mismatched(ii),index) = 1;Mus(mismatched(ii),index,:)             = image(mismatched(ii),:);Sigmas(mismatched(ii),index,:)          = ones(1,CRGB)*Variance;Weights(mismatched(ii),index)           = Props;  endWeights         = Weights./repmat(sum(Weights,2),1,K);active_gaussian = Gaussian+replaced_gaussian;%背景分割 [junk,index]    = sort(Weights./sqrt(Sigmas(:,:,1)),2,'descend');bg_gauss_good   = index(:,1);linear_index    = (index-1)*D+repmat([1:D]',1,K);weights_ordered = Weights(linear_index);for kk = 1:KWeight(:,kk)= sum(weights_ordered(:,1:kk),2);endbg_gauss(:,2:K) = Weight(:,1:(K-1)) < Back_Thresh;bg_gauss(:,1)   = 1;           bg_gauss(linear_index)     = bg_gauss;active_background_gaussian = active_gaussian & bg_gauss;foreground_pixels          = abs(sum(active_background_gaussian,2)-1);foreground_map             = reshape(sum(foreground_pixels,2),RR,CC);Images1(:,:,tt)            = foreground_map;   objects_map                = zeros(size(foreground_map),'int32');object_sizes               = [];Obj_pos                    = [];new_label                  = 1;%计算连通区域[label_map,num_labels]     = bwlabel(foreground_map,8);for label = 1:num_labels object      = (label_map == label);object_size = sum(sum(object));if(object_size >= Comp_Thresh)objects_map             = objects_map + int32(object * new_label);object_sizes(new_label) = object_size;[X,Y]                   = meshgrid(1:CC,1:RR);    object_x                = X.*object;object_y                = Y.*object;Obj_pos(:,new_label)    = [sum(sum(object_x)) / object_size;sum(sum(object_y)) / object_size];new_label               = new_label + 1;endendnum_objects = new_label - 1;%去除阴影index                       = sub2ind(size(Mus),reshape(repmat([1:D],CRGB,1),D*CRGB,1),reshape(repmat(bg_gauss_good',CRGB,1),D*CRGB,1),repmat([1:CRGB]',D,1));background                  = reshape(Mus(index),CRGB,D);background                  = reshape(background',RR,CC,CRGB); background                  = uint8(background);background_Update           = background;background_hsv              = rgb2hsv(background);image_hsv                   = rgb2hsv(vedio{tt});for i = 1:RRfor j = 1:CC      if (objects_map(i,j))&&...(abs(image_hsv(i,j,1)-background_hsv(i,j,1))<SHADOWS(1))&&...(image_hsv(i,j,2)-background_hsv(i,j,2)<SHADOWS(2))&&...(SHADOWS(3)<=image_hsv(i,j,3)/background_hsv(i,j,3)<=SHADOWS(4))Shadows(i,j) = 1;  elseShadows(i,j) = 0;  end               end    end%运动目标检测,二值图Images0                  = objects_map;objecs_adjust_map        = Shadows;Images3                  = objecs_adjust_map;   %运动目标检测-最后处理结果[res3,res4,Xf,Yf,Timef,Wf,Hf] = func_fangk(Images0,vedio{tt},tt,Bg_Length,Ss);%根据上面的结果，提取特征向量%根据上面的结果，提取特征向量Lens  = 800;if length(Xft) < LensXft   = [Xft;Xf];Yft   = [Yft;Yf];Timeft= [Timeft;Timef];Wft   = [Wft;Wf];Hft   = [Hft;Hf];elseXft   = [Xft;Xf];Yft   = [Yft;Yf];Timeft= [Timeft;Timef];Wft   = [Wft;Wf];Hft   = [Hft;Hf];Xft(1:end-800)   = [];Yft(1:end-800)   = [];Timeft(1:end-800)= [];Wft(1:end-800)   = [];Hft(1:end-800)   = [];endfigure(2);subplot(121)imshow(vedio{tt});title('原始图像');subplot(122)imshow(uint8(res3),[]);hold on;if  tt > Bg_Lengthif length(Xft) > LensVX = Xft(length(Xft)-Lens:length(Xft));VY = Yft(length(Xft)-Lens:length(Xft));plot(Xft(length(Xft)-Lens:length(Xft)),Yft(length(Xft)-Lens:length(Xft)),'c.');elseVX = Xft;VY = Yft;plot(Xft,Yft,'c.'); endendtitle('运动目标检测结果');%计算Pdf1tmps    = [Wf,Hf];[Ns,Ds] = size(tmps);Pdf1s   = min(calc_prob(tmps,Ns,2,model.Miu(:,4:5),model.Sigma(4:5,4:5,:),Ds));Pdf1    = mean(Pdf1s);%计算Pdf2if mod(tt,Lens) <= 20tmps = 0;elsetmps = sqrt((Xft(mod(tt,Lens))-Xft(mod(tt,Lens)-20))^2+(Yft(mod(tt,Lens))-Yft(mod(tt,Lens)-20))^2)/20;end[Ns,Ds] = size(tmps);Pdf2s = min(calc_prob(tmps,Ns,2,model.Miu(:,5),model.Sigma(5,5,:),Ds)); Pdf2  = mean(Pdf2s);pause(0.01);%将数据保存，用于后期处理Xf_save{tt}=    Xf;Yf_save{tt}=    Yf;Timef_save{tt}= Timef;Wf_save{tt}=    Wf;Hf_save{tt}=    Hf;Pimages{tt}=    Images0;endsave TEMP\R4.mat  Xf_save Yf_save Timef_save Wf_save Hf_save Pimages

clc;
clear;
close all;
warning off;
addpath 'func\'%通过前面的目标跟踪，这里我们主要对最后的结果进行显示
%本质上C这个程序是B里面的，但是由于涉及到的变量较多，如果再增加一些显示的变量，仿真及其缓慢，而且会出现OUT OF Memory
%所以这里单独对结论进行显示%初始背景的提取
%注意，原始的视频非常大，这里需要截取其中需要处理的帧数
Bg_Length   = 20;
Start_Frame = Bg_Length+1;
End_Frame   = 999;
Simulation_frame = min(800,End_Frame);load EmGmm_model.mat;load TEMP\R4.mat
avi1        = aviread('vedio\4.AVI',[Start_Frame:End_Frame]);
vedio1      = {avi1.cdata};
avi2        = aviread('vedio\4.AVI',[1:Bg_Length]);
vedio2      = {avi2.cdata};
vedio       = [vedio2,vedio1];
tao     = 20;
X_samp  = [];
Y_samp  = [];
T_samp  = [];
W_samp  = [];
H_samp  = [];
X_samps = [];
Y_samps = [];
T_samps = [];
W_samps = [];
H_samps = [];
X_samps2 = [];
Y_samps2 = [];
T_samps2 = [];
W_samps2 = [];
H_samps2 = [];
for tt = 1:Simulation_frame;ttImages0 = Pimages{tt};[res3,res4,Xf,Yf,Timef,Wf,Hf] = func_fangk2(Images0,vedio{tt},tt,Bg_Length);%下面开始进行异常类型识别%重新采样X_samps{tt} = Xf;Y_samps{tt} = Yf;T_samps{tt} = Timef;W_samps{tt} = Wf;H_samps{tt} = Hf;if tt <= tao;X_samp = Xf;Y_samp = Yf;T_samp = Timef;W_samp = Wf;H_samp = Hf;elseX_samp = Xf;Y_samp = Yf;T_samp = 0.001*ones(length(X_samp),1);Ls           = min(length(X_samps{tt}),length(X_samps{tt-tao}));for j = 1:LsT_samp(j,1) = sqrt((X_samps{tt}(j) - X_samps{tt-tao}(j))^2 + (Y_samps{tt}(j) - Y_samps{tt-tao}(j))^2)/tao;endW_samp = Wf;H_samp = Hf;endX_samps2 = [X_samps2;X_samp];Y_samps2 = [Y_samps2;Y_samp];T_samps2 = [T_samps2;T_samp];W_samps2 = [W_samps2;W_samp];H_samps2 = [H_samps2;H_samp];
endfeature = [X_samps2,Y_samps2,T_samps2,W_samps2,H_samps2];
[Ns,Ds] = size(feature);
Px      = calc_prob(feature,Ns,2,model.Miu,model.Sigma,Ds);
[~, cls_ind] = max(Px',[],1); %最后完异常检测效果
XV = [];
YV = [];
for tt = 1:1ttImages0 = Pimages{tt};[res3,res4,Xf,Yf,Timef,Wf,Hf] = func_fangk2(Images0,vedio{tt},tt,Bg_Length);figure(1);subplot(121)imshow(vedio{tt});title('原始图像');subplot(122)imshow(uint8(res3),[]);hold on;for jj = 1:length(cls_ind);if length(find(cls_ind==1)) > 1.2*length(find(cls_ind==2)) if cls_ind(jj)==1plot(X_samps2(jj),Y_samps2(jj),'b.');XV(jj,1) = [X_samps2(jj)];YV(jj,1) = [Y_samps2(jj)];XV(jj,2) = [1];YV(jj,2) = [1];elseplot(X_samps2(jj),Y_samps2(jj),'r.'); XV(jj,1) = [X_samps2(jj)];YV(jj,1) = [Y_samps2(jj)];XV(jj,2) = [2];YV(jj,2) = [2];endendif length(find(cls_ind==1)) <= 1.2*length(find(cls_ind==2)) if cls_ind(jj)==1plot(X_samps2(jj),Y_samps2(jj),'r.');XV(jj,1) = [X_samps2(jj)];YV(jj,1) = [Y_samps2(jj)];XV(jj,2) = [2];YV(jj,2) = [2];elseplot(X_samps2(jj),Y_samps2(jj),'b.');  XV(jj,1) = [X_samps2(jj)];YV(jj,1) = [Y_samps2(jj)];XV(jj,2) = [1];YV(jj,2) = [1];endend        hold on;endtitle('异常行为识别');pause(0.0025);
end%异常检测动画效果，将系统最后处理的结果进行显示
S1 = 0;
E1 = 0;
for tt = 1:Simulation_frame;ttImages0 = Pimages{tt};[res3,res4,Xf,Yf,Timef,Wf,Hf] = func_fangk2(Images0,vedio{tt},tt,Bg_Length);figure(2);subplot(121)imshow(vedio{tt});title('原始图像');subplot(122)imshow(uint8(res3),[]);hold on;S1 = E1 + 1;E1 = E1 + length(Xf);Xtmp = XV(1:E1,1);Ytmp = YV(1:E1,1);Flag = XV(1:E1,2);for j = 1:length(Flag)if Flag(j) == 1plot(Xtmp(j),Ytmp(j),'b.');  hold on;endif Flag(j) == 2plot(Xtmp(j),Ytmp(j),'r.');  hold on;endhold on;endhold on;title('异常行为检测');pause(0.01);
end

3.仿真结果

A10-19

【EM+GMM】基于EM和GMM算法的目标轨迹跟踪和异常行为识别matlab仿真相关推荐

【目标跟踪】基于迭代扩展卡尔曼滤波算法实现目标滤波跟踪（IEKF）附Matlab代码
1 简介 Object tracking is a well studied fifield in computer vision. The goal is to use a sensor sucha ...
DL之SSD：基于tensorflow利用SSD算法实现目标检测(21类)
DL之SSD:基于tensorflow利用SSD算法实现目标检测(21类) 目录输出结果 SSD代码输出结果 VOC_LABELS = {'none': (0, 'Background'),'ae ...
NLP之ASR：基于python和机器学习算法带你玩转的语音实时识别技术
NLP之ASR:基于python和机器学习算法带你玩转的语音实时识别技术导读带你玩转python实现的语音实时识别技术(包括音频混音.回声消除.噪音滤除.杂音消除.静音检测等方法) 视频观看: 软 ...
kcf算法流程_基于运动检测与KCF算法的目标跟踪方法
基于运动检测与 KCF 算法的目标跟踪方法易诗,唐文 [摘要] 摘要:近年来机器视觉技术广泛采用于各个领域,本文设计实现了一种基于运动检测与 KCF 算法的目标跟踪方法,该方法相对于传统单一 ...
基于IMAGE法的房间回响模型创建、C++代码实现、matlab仿真
基于IMAGE法的房间回响模型创建.C++代码实现.matlab仿真 1.模型简介 \qquad在处理声音信号时,我们要对信号先进行采集.那么我们就必须要有,一个发出声音的声源,一个进行声音采集的传感 ...
基于有源钳位三电平的有源电力滤波器（ANPC-APF）MATLAB仿真，包括自建的DSOGI锁相模块和PQ谐波检测模块。可简单解释。
基于有源钳位三电平的有源电力滤波器(ANPC-APF)MATLAB仿真,包括自建的DSOGI锁相模块和PQ谐波检测模块. 可简单解释. YID:1560659846370667tbNick_0m0gm
基于有源钳位三电平的有源电力滤波器（ANPC-APF）MATLAB仿真，包括自建的DSOGI锁相模块和PQ谐波检测模块
基于有源钳位三电平的有源电力滤波器(ANPC-APF)MATLAB仿真,包括自建的DSOGI锁相模块和PQ谐波检测模块. YID:1560659846370667tbNick_0m0gm
基于有源钳位三电平的有源电力滤波器（ANPC-APF）MATLAB仿真
基于有源钳位三电平的有源电力滤波器(ANPC-APF)MATLAB仿真,包括自建的DSOGI锁相模块和PQ谐波检测模块. YYYID:1560659846370667tbNick_0m0gm
基于蒙特卡洛的含分布式电源（风光）的概率潮流计算Matlab仿真
基于蒙特卡洛的含分布式电源(风光)的概率潮流计算Matlab仿真,IEEE33节点,采用牛拉法潮流计算风速光照的概率密度图和网损图以及电压幅值前后对比图. 包括普通版本和matpower可参考. I ...

【EM+GMM】基于EM和GMM算法的目标轨迹跟踪和异常行为识别matlab仿真

1.软件版本

2.系统概述

3.部分源码

3.仿真结果

【EM+GMM】基于EM和GMM算法的目标轨迹跟踪和异常行为识别matlab仿真相关推荐

最新文章

热门文章