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Uploading two human detection methods(python & matlab)
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‎Matlab/CaptureVideo.m‎

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%% Initialization
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% initialize the video writer for saving original video
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OriVideo = VideoWriter('videos/original.avi');
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OriVideo.FrameRate = 10;
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open(OriVideo);
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% % initialize the vidoe writer for saving the detected human video
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% DetVideo = VideoWriter('/Users/Alex/Desktop/HumanDetection/detected.avi');
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% DetVideo.FrameRate = 10 ;
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% open(DetVideo);
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% perameters
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load('models/AcfCaltech+Detector.mat')
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load('M.mat')
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NumberOfFrames = 300;
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% get video input object
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vid = videoinput('macvideo',1);
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triggerconfig(vid, 'manual');
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start (vid)
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set(vid, 'ReturnedColorSpace', 'RGB')
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% shape inserter for draw bbs on detected video
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shapeInserter = vision.ShapeInserter('BorderColor','Custom', ...
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'CustomBorderColor', uint8([0 255 0]), 'LineWidth', 2);
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% % read video
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% xyloObj = VideoReader('/Users/Alex/Desktop/HumanDetection/test/o4_wifi.avi');
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% creat array for saving real detected positions
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real_positions = {};
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% creat struct for saving each frame detections
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Detections = struct(...
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'bx', {}, ...
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'by', {}, ...
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'xp', {}, ...
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'yp', {}, ...
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'ht', {}, ...
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'wd', {}, ...
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'sc', {}, ...
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'xi', {}, ...
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'yi', {}, ...
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'xw', {}, ...
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'yw', {});
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%% detection and show results
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figure
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% for i = 1:xyloObj.NumberOfFrames;
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for i = 1:NumberOfFrames;
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% real time human detection on video
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subplot(1,2,1)
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% % capture frame from web camera
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im = getsnapshot(vid);
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% % capture frame from PETS dataset for multi-tracking
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% im = imread(['img/' sprintf('frame_%04d.jpg', i)]);
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% % capture frame from original video
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% im = read(xyloObj, i);
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% resize image for detection speed
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im = imresize(im, 0.6);
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% write the original video into file 60% image will be loaded quicker
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writeVideo(OriVideo,im);
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% write the calibration image
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if i == 1;
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cali = imresize(im, 0.6);
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imwrite(cali, 'cali.jpg');
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end
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im = imresize(im, 0.6);
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% detect bbs in each frame
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bbs=acfDetect(im, detector); % [x y w h]
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% filter the bbs with score bigger than 50
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indice = bbs(:,5)>45;
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bbs = bbs(indice, :);
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% show result
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imshow(im);
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map = imread('map.jpg');
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% show map on 2nd subplot
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subplot(1,2,2);
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imshow(map);
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hold on;
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% compute human real height
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top = [bbs(:,1)+bbs(:,3)/2, bbs(:,2)];
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bot = [bbs(:,1)+bbs(:,3)/2, bbs(:,2)+bbs(:,4)];
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H = zeros(size(bbs,1), 1);
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P_bot = zeros(4,1);
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P_top = zeros(4,1);
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real_positions = zeros(size(bbs,1),2);
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range = zeros(size(bbs,1),1);
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% loop for each bb
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for j = 1 : size(bbs, 1)
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% get image position of each bounding box
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p_top = [top(j,:),1]';
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p_bot = [bot(j,:),1]';
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% compute real world bbs bottom position
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P_bot_trans = M(:,[1,2,4]) \ p_bot;% inv(M)*p_top
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P_bot_trans = P_bot_trans / P_bot_trans(3);
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P_bot([1,2,4]) = P_bot_trans;
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P_bot(3) = 0;
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% computer real world bbs top height
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P_top_trans = M(:,[2,3,4])\(p_top-5*M(:,1));
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H(j) = P_top_trans(2)/P_top_trans(3);
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% write true human height on video
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subplot(1,2,1);
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text(bbs(j,1),bbs(j,2)-10,['People height=' num2str(H(j))],'FontSize',10,'color','w'); %[sprintf('x=%04d', P_bot(1)) sprintf('y=%04d', P_bot(2))]
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% record all detections
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real_positions(j,:) = P_bot(1:2);
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% % compute whether the bbs in ground truth range or not
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% judge1 = 0.5*bot(j,1)+bot(j,2)-352.5;
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% judge2 = 0.133*bot(j,1)-bot(j,2)+131;
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% if (judge1>0 && judge2>0)
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% range(j) = 1;
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% else
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% range(j) = 0;
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% end
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% draw points on 2-d plane
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if (H(j)>1.25 && H(j)<2.4)
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subplot(1,2,2);
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p_map = [224-P_bot(2)*82/1.4, 356-P_bot(1)*82/1.4];
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plot(p_map(1),p_map(2),'.','MarkerSize',30);
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end
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end
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subplot(1,2,1);
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% draw the passed bbs
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% indice = H>0.5 & H<2.8;
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% bbs = bbs(indice, :);
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% real_positions = real_positions(indice, :);
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% % draw the bbs in ground truth area
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% indice = find(range ==1);
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% bbs = bbs(indice, :);
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bbApply('draw',bbs);
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pause(.05);
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% write detected video
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if ~isempty(bbs)
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rectangles = int32(bbs(:,1:4));
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% rectangles([1,2,3,4],:) = rectangles([2,1,4,3],:);
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DetIm = step(shapeInserter, im, rectangles); % [row column height width]
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else
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DetIm = im;
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end
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% writeVideo(DetVideo,DetIm);
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% Create a new detection
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numOfBoxes = size(bbs, 1);
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bx = zeros(1, numOfBoxes);
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by = zeros(1, numOfBoxes);
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xp = zeros(1, numOfBoxes);
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yp = zeros(1, numOfBoxes);
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ht = zeros(1, numOfBoxes);
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wd = zeros(1, numOfBoxes);
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sc = zeros(1, numOfBoxes);
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xi = zeros(1, numOfBoxes);
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yi = zeros(1, numOfBoxes);
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xw = zeros(1, numOfBoxes);
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yw = zeros(1, numOfBoxes);
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for j = 1: numOfBoxes
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bx(j) = bbs(j, 1);
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by(j) = bbs(j, 2);
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xp(j) = real_positions(1)*1000;
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yp(j) = real_positions(2)*1000;
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ht(j) = bbs(j, 4);
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wd(j) = bbs(j, 3);
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sc(j) = bbs(j, 5);
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xi(j) = bbs(j, 1)+bbs(j, 3)/2;
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yi(j) = bbs(j, 2)+bbs(j, 4);
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xw(j) = xp(j);
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yw(j) = yp(j);
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end
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newDetection = struct(...
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'bx', bx, ... % bounding box left top x (pixel)
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'by', by, ... % bounding box left top y (pixel)
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'xp', xp, ... % bottom center real world position x (mm)
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'yp', yp, ... % bottom center real world position y (mm)
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'ht', ht, ... % bounding box height (pixel)
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'wd', wd, ... % bounding box width (pixel)
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'sc', sc, ... % score of each bounding box
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'xi', xi, ... % bottom center image position x (pixel)
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'yi', yi, ... % bottom center image position y (pixel)
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'xw', xw, ... % same as xp
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'yw', yw); % same as yp
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% add new detection to Detections
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Detections(end+1) = newDetection;
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end
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% % close the opened video writer
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close(OriVideo);
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% close(DetVideo);
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% % close camera input object
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stop(vid)
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delete(imaqfind)
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% I = imread('/Users/Alex/Desktop/HumanDetection/one.jpg');
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%
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% bbs=acfDetect(I, detector);
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% figure(1); im(I); bbApply('draw',bbs); pause(.1);

‎Matlab/README.md‎

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%%
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% Please read this file before using the 'kalman.m' function to track
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% objects
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% 1) There are four important m files:
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% *--- 'calibration.m': calibrate the camera, key points must be
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% pointed out from bottom right, counterclockwise, base to top.
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% *--- 'CaptureVideo.m': capture video from web camera in real time, and
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% the captured video is saved in the file named 'videos' as
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% 'original.avi', and the first frame of the video is saved as 'cali.jpg'
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% for camera calibration.
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% *--- 'ShowCapturedVideo.m': show the detection result of any specific
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% video, and the input video's path is set by the parameter
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% 'ShowWhichVideoResult'.
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% *--- 'kalman.m': show the tracking result of any specific video, and
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% the input video's path is set by the parameter 'TrackWhichVideo'
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% 2) show/not show bounding box score is determined by the function 'bbApply.m'
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% at line 300.
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% 3) When calibrate the camera, just run the 'calibration.m' file and it
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% will use the lastest 'cali.jpg' as input image.
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