Code covered by the BSD License  

Highlights from
January 2013 "Computer Vision with MATLAB" webinar demo files

image thumbnail

January 2013 "Computer Vision with MATLAB" webinar demo files

by

 

MATLAB code used in the computer vision webinar held on January 29, 2013.

visionfacetrackingKLT.m
%% Face Detection and Tracking Using the KLT Algorithm
% This example shows how to automatically detect and track a face using
% feature points. The approach in this example keeps track of the face even
% when the person tilts his or her head, or moves toward or away from the
% camera.
%
%   Copyright 2013 The MathWorks, Inc.

%% Detect a Face
% Create a cascade detector object.
faceDetector = vision.CascadeObjectDetector();

% Open a video file
videoFileReader = vision.VideoFileReader('tilted_face.avi');

% Read a video frame and run the face detector.
videoFrame      = step(videoFileReader);
bbox            = step(faceDetector, videoFrame);

% Convert the box to a polygon. This is needed to be able to visualize the
% rotation of the object.
x = bbox(1); y = bbox(2); w = bbox(3); h = bbox(4);
bboxPolygon = [x, y, x+w, y, x+w, y+h, x, y+h];

% Draw the returned bounding box around the detected face.
shapeInserter  = vision.ShapeInserter('Shape', 'Polygons', 'BorderColor','Custom',...
    'CustomBorderColor',[255 255 0]);
videoFrame = step(shapeInserter, videoFrame, bboxPolygon);
figure; imshow(videoFrame); title('Detected face');

%% Identify Facial Features To Track
% Crop out the region of the image containing the face, and detect the
% feature points inside it.
cornerDetector = vision.CornerDetector('Method', ...
    'Minimum eigenvalue (Shi & Tomasi)');
points = step(cornerDetector, rgb2gray(imcrop(videoFrame, bbox)));

% The coordinates of the feature points are with respect to the cropped 
% region. They need to be translated back into the original image
% coordinate system. 
points = double(points);
points(:, 1) = points(:, 1) + double(bbox(1));
points(:, 2) = points(:, 2) + double(bbox(2));

% Display the detected points.
markerInserter = vision.MarkerInserter('Shape', 'Plus', ...
    'BorderColor', 'White');
videoFrame = step(markerInserter, videoFrame, points);
figure, imshow(videoFrame), title('Detected features');

%% Initialize a Tracker to Track the Points
% Create a point tracker and enable the bidirectional error constraint to
% make it more robust in the presence of noise and clutter.
pointTracker = vision.PointTracker('MaxBidirectionalError', 2);

% Initialize the tracker with the initial point locations and the initial
% video frame.
initialize(pointTracker, double(points), rgb2gray(videoFrame));

%% Initialize a Video Player to Display the Results
% Create a video player object for displaying video frames.
videoInfo    = info(videoFileReader);
videoPlayer  = vision.VideoPlayer('Position',...
    [100 100 videoInfo.VideoSize(1:2)+30]);

%% Initialize a Geometric Transform Estimator
geometricTransformEstimator = vision.GeometricTransformEstimator(...
    'PixelDistanceThreshold', 4, 'Transform', 'Nonreflective similarity');

% Make a copy of the points to be used for computing the geometric
% transformation between the points in the previous and the current frames
oldPoints = double(points);

%% Track the Points
while ~isDone(videoFileReader)
    % get the next frame
    videoFrame = step(videoFileReader);

    % Track the points. Note that some points may be lost.
    [points, isFound] = step(pointTracker, rgb2gray(videoFrame));
    visiblePoints = points(isFound, :);
    oldInliers = oldPoints(isFound, :);
    
    if ~isempty(visiblePoints)
        % Estimate the geometric transformation between the old points
        % and the new points.
        [xform, geometricInlierIdx] = step(geometricTransformEstimator, ...
            double(oldInliers), double(visiblePoints));
        
        % Eliminate outliers
        visiblePoints = visiblePoints(geometricInlierIdx, :);
        oldInliers = oldInliers(geometricInlierIdx, :);
        
        % Apply the transformation to the bounding box
        boxPoints = [reshape(bboxPolygon, 2, 4)', ones(4, 1)];
        boxPoints = boxPoints * xform;
        bboxPolygon = reshape(boxPoints', 1, numel(boxPoints));
        
        % Insert a bounding box around the object being tracked
        videoFrame = step(shapeInserter, videoFrame, bboxPolygon);
        
        % Display tracked points
        videoFrame = step(markerInserter, videoFrame, visiblePoints);
        
        % Reset the points
        oldPoints = visiblePoints;
        setPoints(pointTracker, oldPoints);        
    end
    
    % Display the annotated video frame using the video player object
    step(videoPlayer, videoFrame);
end

%% Clean up
release(videoFileReader);
release(videoPlayer);
release(geometricTransformEstimator);
release(pointTracker);
close all

Contact us