关于MATLAB中多目标的跟踪。

下面是camshift物体追踪代码，需要你用avi视频测试，matlab对avi视频格式要求比较严格。但是可以试试mmreader函数读取视频。

% Adam Kukucka

% Zach Clay

% Marcelo Molina

% CSE 486 Project 3

function [ trackmov probmov centers ] = camshift

% ******************************************************************

% initialize vari ables

% ******************************************************************

rmin = 0%min row value for search window

rmax = 0%max row value for search window

cmin = 0%min col value for search window

cmax = 0%max col value for search window

numofframes = 0%number of frames in the avi

threshold = 1%threshold for convergence

centerold = [0 0]%for convergence... previous center of window

centernew = [0 0]%for convergence... new center of window

% ******************************************************************

% Pre code... load movie and select initial frame

% ******************************************************************

% prompt user for avi file name

%%%%%user_entry = input('Please enter an avi filename: ','s')

% load the avi file... handle is M

%%%%M = aviread(user_entry)

M=aviread('8888.avi')

% get number of frames

[dontneed numberofframes] = size(M)

% initialize matrix to hold center coordinates

imagecenters = zeros(numberofframes, 2)

% extract the first frame from the avi

Frame1 = M(1,1)

Image1 = frame2im(Frame1)

%%% ********** images(:, :, numberofframes) = G(:,:)

% get search window for first frame

[ cmin, cmax, rmin, rmax ] = select( Image1 )

cmin = round(cmin)

cmax = round(cmax)

rmin = round(rmin)

rmax = round(rmax)

wsize(1) = abs(rmax - rmin)

wsize(2) = abs(cmax - cmin)

% create histogram

% translate to hsv

hsvimage = rgb2hsv(Image1)

% pull out the h

huenorm = hsvimage(:,:,1)

% scale to 0 to 255

hue = huenorm*255

% set unit type

hue=uint8(hue)

% Getting Histogram of Image:

histogram = zeros(256)

for i=rmin:rmax

for j=cmin:cmax

index = uint8(hue(i,j)+1)

%count number of each pixel

histogram(index) = histogram(index) + 1

end

end

% ******************************************************************

% Algorithm from pdf

% ******************************************************************

aviobj1 = avifile('example3.avi')

aviobj2 = avifile('example4.avi')

% for each frame

for i = 1:200

disp('Processing frame')

disp(i)

Frame = M(1, i)

I = frame2im(Frame)

% translate to hsv

hsvimage = rgb2hsv(I)

% pull out the h

huenorm = hsvimage(:,:,1)

% scale to 0 to 255

hue = huenorm*255

% set unit type

hue=uint8(hue)

[rows cols] = size(hue)

% choose initial search window

% the search window is (cmin, rmin) to (cmax, rmax)

% create a probability map

probmap = zeros(rows, cols)

for r=1:rows

for c=1:cols

if(hue(r,c) ~= 0)

probmap(r,c)= histogram(hue(r,c))

end

end

end

probmap = probmap/max(max(probmap))

probmap = probmap*255

count = 0

rowcenter = 0 % any number just so it runs through at least twice

colcenter = 0

rowcenterold = 30

colcenterold = 30

% Mean Shift for 15 iterations or until convergence(the center doesnt

% change)

while (((abs(rowcenter - rowcenterold) >2) &&(abs(colcenter - colcenterold) >2)) || (count <15) )

%for j = 1:5

%disp('meanshift')

% disp(j)

rmin = rmin - 7 %increase window size and check for center

rmax = rmax + 7

cmin = cmin - 7

cmax = cmax + 7

rowcenterold = rowcenter%save old center for convergence check

colcenterold = colcenter

[ rowcenter colcenter M00 ] = meanshift(I, rmin, rmax, cmin,...

cmax, probmap)

% given image (I), search window(rmin rmax cmin cmax)

% returns new center (colcenter, rowcenter) for window and

% zeroth moment (Moo)

% redetermine window around new center

rmin = round(rowcenter - wsize(1)/2)

rmax = round(rowcenter + wsize(1)/2)

cmin = round(colcenter - wsize(2)/2)

cmax = round(colcenter + wsize(2)/2)

wsize(1) = abs(rmax - rmin)

wsize(2) = abs(cmax - cmin)

count = count + 1

end

% mark center on image

%save image

G = .2989*I(:,:,1)...

+.5870*I(:,:,2)...

+.1140*I(:,:,3)

trackim=G

%make box of current search window on saved image

for r= rmin:rmax

trackim(r, cmin) = 255

trackim(r, cmax) = 255

end

for c= cmin:cmax

trackim(rmin, c) = 255

trackim(rmax, c) = 255

end

aviobj1 = addframe(aviobj1,trackim)

aviobj2 = addframe(aviobj2,probmap)

%create image movie, and probability map movie

trackmov(:,:,i)= trackim(:,:)

probmov(:,:,i) = probmap(:,:)

% save center coordinates as an x, y by doing col, row

centers(i,:) = [colcenter rowcenter]

% Set window size = 2 * (Moo/256)^1/2

windowsize = 2 * (M00/256)^.5

% get side length ... window size is an area so sqrt(Area)=sidelength

sidelength = sqrt(windowsize)

% determine rmin, rmax, cmin, cmax

rmin = round(rowcenter-sidelength/2)

rmax = round(rowcenter+sidelength/2)

cmin = round(colcenter-sidelength/2)

cmax = round(colcenter+sidelength/2)

wsize(1) = abs(rmax - rmin)

wsize(2) = abs(cmax - cmin)

end

% end for loop

% Adam Kukucka

% Zach Clay

% Marcelo Molina

% CSE 486 Project 3

function [ rowcenter colcenter M00 ] = meanshift(I, rmin, rmax, cmin,...

cmax, probmap)

%inputs

% rmin, rmax, cmin, cmax are the coordiantes of the window

% I is the image

%outputs

% colcenter rowcenter are the new center coordinates

% Moo is the zeroth mean

% **********************************************************************

% initialize

% **********************************************************************

M00 = 0%zeroth mean

M10 = 0%first moment for x

M01 = 0%first moment for y

histdim = (0:1:255)% dimensions of histogram... 0 to 255, increment by 1

[rows cols] = size(I)

cols = cols/3% **********************8

% **********************************************************************

% Main code

% **********************************************************************

% determine zeroth moment

for c = cmin:cmax

for r = rmin:rmax

M00 = M00 + probmap(r, c)

end

end

% determine first moment for x(col) and y(row)

for c = cmin:cmax

for r = rmin:rmax

M10 = M10 + c*probmap(r,c)

M01 = M01 + r*probmap(r,c)

end

end

% determine new centroid

% x is cols

colcenter = M10/M00

% y is rows

rowcenter = M01/M00

% Adam Kukucka

% Zach Clay

% Marcelo Molina

% CSE 486 Project 3

function [ cmin, cmax, rmin, rmax ] = select( I )

%UNTITLED1 Summary of this function goes here

% Detailed explanation goes here

% for array... x is cols, y is rows

image(I)

k = waitforbuttonpress

point1 = get(gca,'CurrentPoint') %mouse pressed

rectregion = rbbox

point2 = get(gca,'CurrentPoint')

point1 = point1(1,1:2) % extract col/row min and maxs

point2 = point2(1,1:2)

lowerleft = min(point1, point2)

upperright = max(point1, point2)

cmin = round(lowerleft(1))

cmax = round(upperright(1))

rmin = round(lowerleft(2))

rmax = round(upperright(2))

可以用复数表示，比如x=[1+j；2+2j；3+3j]。

坐标是一维坐标x还是二则型维坐标(x，y)？如果是孙迅猜前者，就用plot(t，x)；如果是后者，就用plot3(t，x，y)。其中，t为每个坐标对应的时刻。

试试下面的效果是不是你要的

clearall

clc

x=0：pi/50：2*pi；

y=sin(x)；

plot(x，y)

h=line('xdata'，[]，'ydata'，[]，'color'，'r'，'marker'，'。'，'markersize'，30)；

forii=1：length(x)

set(h，'xdata'，x(ii)，'ydata'，y(ii))；

扩展资料：

x'表达方式

>昌睁 >X is equal to rand of 6.

X＝

0．18690．27600．95970．5060

0．04620．03440．48980．67970．34040．6991

Thereisnoneedtomakeadifference

0．64630．16260．22380．9593

0．69480．76550．70940．11900．75130．5472

0．75470．49840．25510．1386

>>X(2:4, [2, 3, 5])

Ans＝

0．03440．48980．3404

0．43870．44560．5853

0．38160．64630．2238

>>

可靠，所以现在主要以手势/人脸识别为主；这是因为手和脸上面有比较独特的特征点。你说的滤波归根结底还是要找出具有灰度跳变的高频部分作为人体；这除非背景中除了人以外没有其他突出的物体；否则光凭滤波二值法检测人体是不太现实。

2 两张图片中人要是产生相对运动，检测起来就容易多了；利用帧间差分找到图像中灰度相差大的部分（你用的滤波也是一种手段）；然后二值化区域连通；要是图像中没有其他移动物体计算枯销磨连通区域的变动方向就是人的运动方向。

你可以没斗去PUDN上搜搜相关的目标检测的代码完全和你这个对应是不可能的。照你说的情况可以先建立起静态背景的模型（或者直接在没人的时候拍张）；然后不断的与这个背景做差，原理和帧间差分一样。建议你先从典型的帧间差分例程开始下手（比如移动车辆的检测，这个比较多）。

你在二值化之后加上一个区域连通的步骤；即使用膨胀或者闭运算；这样你的轮廓就是连续的了；用matlab的话bwlabel可以统计连通区域里面像素的个数也就是人体面斗尺积大小。质心就是横竖坐标的平均值；取所有人体点的横竖坐标分别累加；除以坐标总数得到的x和y平均值；这个就是质心了

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