使用卡尔曼滤波器进行背景减法和前景检测

Question

I need to separate the background from the foreground in a video using Kalman filter. Can somebody give me some resources or code examples to follow.

Update: i've found a good example here Traffic detection .It worked excellent for Traffic detection but i want to re-adapt it for people extraction. I've found some variables that's need to be adapted for example:

1. alpha = learning constant
2. K= #of guassians in the mixture
3. T = min. portion of background
4. initVariance = initial variance
5. pixelThresh = threshold condition for computing adaptive process on pixel

Here is an overview of the main file (In case just you want an overview)

---

function foregroundEstimation(movie)

%Load video into memory and prepare output video for writing

v = VideoReader(movie);
numFrames = 150;
foregroundVideo = VideoWriter('foreground.avi');
open(foregroundVideo);

%video constants

initFrame = read(v,1);
global height;
global width;
height = size(initFrame,1); 
width = size(initFrame,2);



%initialize GMM parameters: (based upon Stauffer notation)
%http://www.ai.mit.edu/projects/vsam/Publications/stauffer_cvpr98_track.pdf

% alpha = learning constant
%  K= #of guassians in the mixture
% T = min. portion of background
% initVariance = initial variance
% pixelThresh = threshold condition for computing adaptive process on pixel

alpha=0.05;  % 0.05
K=5;         % 5
global T;    % T = 0.8
T=0.8;

global initVariance;
initVariance=75; % 75
pixelThresh=45; % 45
referenceDistance = 40; % 40 %shortcut to speed up processing time. Compare current pixel to pixel referenceDistance frames back and skip adaptive process if similar. Downside is doesn't collect background evidence as well.
sideWeight = (K-1)/(1-T);
global matchThres;
matchThres = sqrt(3);
global ccThreshold;
ccThreshold = 9000;  % 5000
global deltaT;
deltaT = 1;
global numParticles;
numParticles = 100;

trackingOn = 0; % will superimpose tracking color marker on detected vehicles in output video. tackingOn should equal 1 or 0

prevCentSize = 0;


%structures to pass information between frames for detection purposes.
field = 'f';
filterValue = {[];[];};
prevFilter = struct(field,filterValue);
modelValue = {[];prevFilter};
prevModel = struct(field,modelValue);

%initiailze video process components
initColorBoxes();


foreFrame = zeros(height,width,3);
backFrame = zeros(height,width,3);

%map represents pixels at a given frame to perform adaptive process
pixThreshMap = ones(height,width);


%individual pixel process components
pixel = zeros(3,1);
pixMean = zeros(3,1,K);
pixVar = ones(1,K);
pixWeight = zeros(1,K);

%global pixel process components
globalWeight = (ones(height,width,K)/sideWeight);
globalWeight(:,:,1) = T;
%globalWeight = (ones(height,width,K)/K);
globalMean = zeros(height,width,3,K);
globalVar = ones(height,width,K);




%=====================================================
%Extract Foreground and Background by K-mixture model
%=====================================================
%initialize g-mixture model
globalVar = globalVar*initVariance;

for k=1:K
globalMean(:,:,1,k)=initFrame(:,:,1);
globalMean(:,:,2,k)=initFrame(:,:,2);
globalMean(:,:,3,k)=initFrame(:,:,3);
end;


distVec = zeros(numFrames,1);

%adaptive g-mixture background segmentation
for frameIndex=2:numFrames
%get current frame and the refernece frame
%tic;
frameIndex
currentFrame = double(read(v,frameIndex));



if (frameIndex<=referenceDistance)
referenceFrame= double(read(v,1));
else
referenceFrame= double(read(v,frameIndex-referenceDistance));
end;

frameDifference = abs(currentFrame - referenceFrame);

%creates map of pixel locations where we will perform adaptive process. Based
%upon threshold that detects low change regions based on previous frame in
%order to save computation.
pixThreshMap = min(sum(+(frameDifference(:,:,:)>pixelThresh),3),1);



for index=1:3
  backFrame(:,:,index)=(+(pixThreshMap(:,:)==0)).*currentFrame(:,:,index);      
end;

%extract the parts considered "stable background" from current frame

%reset foreground frame
foreFrame = ones(height,width,3)*255;

%gaussian mixture matching & model updating



[i,j]=find(pixThreshMap(:,:)==1);


%loop through every pixel location where adaptive process should be performed
for k = 1:size(i,1)

    pixel = reshape(currentFrame(i(k),j(k),:),3,1);
    pixMean = reshape(globalMean(i(k),j(k),:,:),3,1,K);
    pixVar = reshape(globalVar(i(k),j(k),:,:),1,K);
    pixWeight=reshape(globalWeight(i(k),j(k),:),1,K);

    %update gaussian mixture according to new pix value
    match=matchingCriterion(pixMean,pixVar,pixel);
    matchWeight = 0;

    if(match>0)
        %match found so update weights/normalize
        pixWeight = (1-alpha)*pixWeight;
        pixWeight(match)= pixWeight(match) + alpha;
        pixWeight = pixWeight/sum(pixWeight);
        matchWeight = pixWeight(1,match);

        %NOTE ALPHA SHOULD BE REPACED WITH SOME KIND OF RHO EVENTUALLY
        %WHERE RHO IS PRODUCT OF ALPHA AND CONDITIONAL PROBABILITY MEASURE

        %update variance
        pixVar(:,match) = (1-alpha)*pixVar(:,match) + ...
        alpha*(pixel - pixMean(:,:,match))'*(pixel-pixMean(:,:,match));

        %update mean
        pixMean(:,:,match) = (1-alpha)*pixMean(:,:,match) + alpha*pixel;



    else
        %no match currently found.
        %replace one with lowest sigma value

        rankVector = pixWeight./sqrt(pixVar(1,:));
        [mini minIndex] = min(rankVector);

        pixMean(:,:,minIndex) = pixel;  
        pixVar(:,minIndex) = initVariance;  
    end

    %rerank all pixel components
    rankCriterionVector = pixWeight./sqrt(pixVar(1,1,:)); 
    [rankSort rankIndex] = sort(rankCriterionVector);

    pixMean = pixMean(rankIndex);
    pixVar = pixVar(rankIndex);
    pixWeight = pixWeight(rankIndex);


    %repopulate global structures with updated values
    globalWeight(i(k),j(k),:) = pixWeight(:,1);
    globalMean(i(k),j(k),:,:) = pixMean(:,1,:);
    globalVar(i(k),j(k),:,:) = pixVar(:,:,:);

    %now need to perform the background segmentation based upon weight
    %threshold

    bgIndex = segmentBackground(pixWeight);
    if(ismember(matchWeight, pixWeight))
        matchIndex = find(pixWeight == matchWeight,1);
    else
        matchIndex = 0;
    end

    if((matchIndex>=bgIndex) || (matchIndex == 0))

        %also check against initFrame for match
        %NOTE CHANGE
        if(initMatch(initFrame(i(k),j(k),:),pixel) == 0)
          foreFrame(i(k),j(k),:) = pixel;   
        end 

    end


end


%Now write foreground frame to foreground estimation video
contrastFrame = foreFrame/max(abs(foreFrame(:)));

%remove all connected components associated with foreground objects that are smaller than what we expect a vehicle should be.
[cleanFrame centroids]= connectedComponentCleanup(contrastFrame);

if(trackingOn == 1)
if(size(centroids,1) > prevCentSize)
   prevModel = addModel(prevModel, centroids, height, width);
elseif (size(centroids,1) < prevCentSize)
   prevModel = removeModel(prevModel, centroids, height, width);
end


if(size(centroids,1) > 0)
    %implies there is a car in frame for tracking
    [curModel orderedCentroids] = vehicleTracking(centroids, prevModel);
    prevModel = curModel;
    trackFrame = colorBox(cleanFrame, curModel,orderedCentroids, height, width);  
else
    trackFrame = cleanFrame;
end

else
   trackFrame = cleanFrame; 
end

writeVideo(foregroundVideo,trackFrame); 
prevCentSize = size(centroids,1);
end

---

Thanks.

Answer 1

Basically this is not task for kalman filter, look at background subtraction in opencv. Kalman filter could be used after that to track objects between frames, you could look for example here http://studentdavestutorials.weebly.com/kalman-filter-with-matlab-code.html