matlab2015b
算法概述
人群密度分为三个等级，(1)在稀缺和不拥挤的情况下提醒绿色。(2)在拥挤的情况下发出黄色警告。(3)在非常拥挤的情况下发出红色警报。 通过相应的报警级别在界面上实时显示不同人群的密度
人群密度分类有两种思路:
(1)估计在景人数，根据人数判断人群密度。
(2)提取分析人群的整体特征，训练样本，用分类器学习分类。
首先，提取视频的纹理是灰度共生矩阵：
http://wenku.baidu.com/view/d60d9ff5ba0d4a7302763ae1.html?from=search
然后通过GRNN神经网络训练识别算法：
广义回归神经网络(Generalized regression neural network, GRNN)通过观察样本计算自变量与因变量之间的概率密度函数，是一种基于非参数核回归的神经网络。GRNN结构如图1所示，整个网络包括四层神经元：输入层、模式层、求和层与输出层。
GRNN神经网络的性能主要由隐藏回归单元核函数的光滑因子设置，不同的光滑因子可以获得不同的网络性能。输入层的神经元数等于学习样本中输入向量的维数m。每个神经元对应一个不同的学习样本，模式层中第一个神经元的传输函数为：
由此可见，选择学习样本后，GRNN网络的结构和权重是完全确定的，所以训练GRNN网络比训练更重要BP网络和RBF网络要方便得多。根据上述情况。GRNN网络各层的输出计算公式，整个GRNN网络输出可以用如公式表示：
部分源码
function pushbutton2_Callback(hObject, eventdata, handles) % hObject handle to pushbutton2 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global frameNum_Original; global frameNum_Originals; global Obj; %% %参数初始化 视频大小处理% RR = 200; CC = 300; K = 3; %组件 Alpha = 0.02; %适应权重速度 Rho = 0.01; 适应权重速度的% Deviation_sq = 49; 用阈值用于搜索匹配 Variance = 2; 新放置组件初始方差% Props = 0.00001; %最初是新放置的 Back_Thresh = 0.8; 背景模型必须占%体重的比例 Comp_Thresh = 10; %过滤连接组件的小尺寸 SHADOWS =[0.7,0.25,0.85,0.95]; %设置阴影去除门限值 CRGB = 3; D = RR * CC; Temps = zeros(RR,CC,CRGB,'uint8'); Temps = imresize(read(Obj,1),[RR,CC]); Temps = reshape(Temps,size(Temps,1)*size(Temps,2),size(Temps,3)); Mus = zeros(D,K,CRGB); Mus(:,1,:) = double(Temps(:，，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,frameNum_Original); Shadows = zeros(RR,CC); Images0 = zeros(RR,CC,frameNum_Original); Images1 = zeros(RR,CC,frameNum_Original); Images2 = zeros(RR,CC,frameNum_Original); background_Update = zeros(RR,CC,CRGB,frameNum_Original); indxx = 0; for tt = frameNum_Originals disp(当前帧数)； tt indxx = indxx 1; pixel_original = read(Obj,tt); pixel_original2 = imresize(pixel_original,[RR,CC]); Temp = zeros(RR,CC,CRGB,'uint8'); Temp = pixel_original2; Temp = reshape(Temp,size(Temp,1)*size(Temp,2),size(Temp,3)); image = Temp; for kk = 1:K Datac = double(Temp)-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:K pixel_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)); end replaced_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;
end
Weights = 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:K
Weight(:,kk)= sum(weights_ordered(:,1:kk),2);
end
bg_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 = 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;
end
end
num_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);
if indxx <= 500;
background_Update = background;
else
background_Update = background_Update;
end
background_hsv = rgb2hsv(background);
image_hsv = rgb2hsv(pixel_original2);
for i = 1:RR
for 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;
else
Shadows(i,j) = 0;
end
end
end
Images0 = objects_map;
objecs_adjust_map = Shadows;
Images2 = objecs_adjust_map;
%%
%根据像素所在区域大小比例以及纹理特征分析获得人密度
%腐蚀处理
se = strel('ball',6,6);
Images2BW = floor(abs(imdilate(Images2,se)-5));
Images3BW = zeros(size(Images2BW));
X1 = round(168/2);
X2 = round(363/2);
Y1 = round(204/2);
Y2 = round(339/2);
if indxx > 80;
%计算区域内像素值
S1 = sum(sum(Images2BW(Y1:Y2,X1:X2)));
S2(indxx-80) = S1/((X2-X1)*(Y2-Y1));
end
Images3BW(Y1:Y2,X1:X2) = Images2BW(Y1:Y2,X1:X2);
Images3Brgb = pixel_original2(Y1:Y2,X1:X2,:);
%纹理检测
%计算纹理
[A,B] = func_wenli(rgb2gray(Images3Brgb));
%选择能量 熵作为判断依据
if indxx > 80;
F1(indxx-80) = A(1);
F2(indxx-80) = A(2);
F3(indxx-80) = A(3);
end
if indxx > 80;
load train_model.mat
P = [S2(indxx-80);F2(indxx-80)];
y = round(NET(P));
if y == 1
set(handles.text2,'String','低密度');
set(handles.text2,'ForegroundColor',[0 1 0]) ;
end
if y == 2
set(handles.text2,'String','中密度');
set(handles.text2,'ForegroundColor',[1 1 0]) ;
end
if y == 3
set(handles.text2,'String','高密度');
set(handles.text2,'ForegroundColor',[1 0 0]) ;
end
end
axes(handles.axes1)
imshow(pixel_original2);
% title('定位检测区域');
hold on
line([X1,X2],[Y1,Y1],'LineWidth',1,'Color',[0 1 0]);
hold on
line([X2,X2],[Y1,Y2],'LineWidth',1,'Color',[0 1 0]);
hold on
line([X2,X1],[Y2,Y2],'LineWidth',1,'Color',[0 1 0]);
hold on
line([X1,X1],[Y2,Y1],'LineWidth',1,'Color',[0 1 0]);
axes(handles.axes2)
imshow(uint8(background_Update));
% title('背景获得');
axes(handles.axes3)
imshow(Images0,[]);
% title('动态背景提取');
axes(handles.axes4)
imshow(Images3BW,[]);
% title('动态背景提取(检测区域内)');
pause(0.0000001);
end