1.软件版本
matlab2015b
2.算法概述
人群密度情况分为三个等级，（1）稀少和不拥挤情况下为绿色提醒。（2）比较拥挤情况下，黄色预警。（3）非常拥挤情况下，红色报警。 不同人群密度情况通过相应的报警级别在界面上实时显示出来
人群密度分类两种思路：
（1）估计在景人数，根据人数多少，判断人群密度情形。
（2）提取分析人群的整体特征，训练样本，利用分类器学习分类。
首先对视频进行纹理提取，采用的方法是灰度共生矩阵：
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)是一种建立在非参数核回归基础之上的神经网络，通过观测样本计算自变量和因变量之间的概率密度函数。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，主要通过对其隐回归单元的核函数的光滑因子来设置的，不同的光滑因子可获得不同的网络性能。输入层的神经元数目与学习样本中输入向量的维数m相等。每个神经元都分别对应一个不同的学习样本，模式层中第i个神经元的传递函数为：
由此可以看出，当选择出学习样本之后，GRNN网络的结构与权值都是完全确定的，因而训练GRNN网络要比训练BP网络和RBF网络便捷得多。根据上述GRNN网络的各个层的输出计算公式，整个GRNN网络的输出可用如的式子表示：
3.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
-
%处理视频大小
-
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 =
0.8; %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]; %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);
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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
4.仿真结果
转载：https://blog.csdn.net/ccsss22/article/details/125568328
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