face_track.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.
%% Introduction
% Object detection and tracking are important in many computer vision applications 
% including activity recognition, automotive safety, and surveillance. In this 
% example, you will develop a simple face tracking system by dividing the tracking 
% problem into three parts: 
% 
% # Detect a face
% # Identify facial features to track 
% # Track the face
%% Detect a Face
% First, you must detect the face. Use the |vision.CascadeObjectDetector| System 
% object? to detect the location of a face in a video frame. The cascade object 
% detector uses the Viola-Jones detection algorithm and a trained classification 
% model for detection. By default, the detector is configured to detect faces, 
% but it can be used to detect other types of objects. 

% Create a cascade detector object.
faceDetector = vision.CascadeObjectDetector();
dataDir = './data';
vidFile = fullfile(dataDir,'ican.mp4');
% Read a video frame and run the face detector.
videoFileReader = vision.VideoFileReader(vidFile);
videoFrame      = step(videoFileReader);
bbox            = step(faceDetector, videoFrame);

% Draw the returned bounding box around the detected face.
videoFrame = insertShape(videoFrame, 'Rectangle', bbox);
figure; imshow(videoFrame); title('Detected face');

% Convert the first box into a list of 4 points
% This is needed to be able to visualize the rotation of the object.
bboxPoints = bbox2points(bbox(1, :));
%% 
% To track the face over time, this example uses the Kanade-Lucas-Tomasi 
% (KLT) algorithm. While it is possible to use the cascade object detector on 
% every frame, it is computationally expensive. It may also fail to detect the 
% face, when the subject turns or tilts his head. This limitation comes from the 
% type of trained classification model used for detection. The example detects 
% the face only once, and then the KLT algorithm tracks the face across the video 
% frames. 
%% Identify Facial Features To Track
% The KLT algorithm tracks a set of feature points across the video frames. 
% Once the detection locates the face, the next step in the example identifies 
% feature points that can be reliably tracked. This example uses the standard, 
% "good features to track" proposed by Shi and Tomasi. 
%%
% Detect feature points in the face region.
points = detectMinEigenFeatures(rgb2gray(videoFrame), 'ROI', bbox);

% Display the detected points.
figure, imshow(videoFrame), hold on, title('Detected features');
plot(points);
%% Initialize a Tracker to Track the Points
% With the feature points identified, you can now use the |vision.PointTracker| 
% System object to track them. For each point in the previous frame, the point 
% tracker attempts to find the corresponding point in the current frame. Then 
% the |estimateGeometricTransform| function is used to estimate the translation, 
% rotation, and scale between the old points and the new points. This transformation 
% is applied to the bounding box around the face.
%%
% 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.
points = points.Location;
initialize(pointTracker, points, videoFrame);
%% Initialize a Video Player to Display the Results
% Create a video player object for displaying video frames.
%%
videoPlayer  = vision.VideoPlayer('Position',...
    [100 100 [size(videoFrame, 2), size(videoFrame, 1)]+30]);
%% Track the Face
% Track the points from frame to frame, and use |estimateGeometricTransform| 
% function to estimate the motion of the face.
%%
% 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 = 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, videoFrame);
    visiblePoints = points(isFound, :);
    oldInliers = oldPoints(isFound, :);
    
    if size(visiblePoints, 1) >= 2 % need at least 2 points
        
        % Estimate the geometric transformation between the old points
        % and the new points and eliminate outliers
        [xform, oldInliers, visiblePoints] = estimateGeometricTransform(...
            oldInliers, visiblePoints, 'similarity', 'MaxDistance', 4);
        
        % Apply the transformation to the bounding box points
        bboxPoints = transformPointsForward(xform, bboxPoints);
                
        % Insert a bounding box around the object being tracked
        bboxPolygon = reshape(bboxPoints', 1, []);
        videoFrame = insertShape(videoFrame, 'Polygon', bboxPolygon, ...
            'LineWidth', 2);
                
        % Display tracked points
        videoFrame = insertMarker(videoFrame, visiblePoints, '+', ...
            'Color', 'white');       
        
        % 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(pointTracker);
%% Summary
% In this example, you created a simple face tracking system that automatically 
% detects and tracks a single face. Try changing the input video, and see if you 
% are still able to detect and track a face. Make sure the person is facing the 
% camera in the initial frame for the detection step.
%% References
% Viola, Paul A. and Jones, Michael J. "Rapid Object Detection using a Boosted 
% Cascade of Simple Features", IEEE CVPR, 2001.
% 
% Bruce D. Lucas and Takeo Kanade. An Iterative Image Registration Technique 
% with an Application to Stereo Vision. International Joint Conference on Artificial 
% Intelligence, 1981.
% 
% Carlo Tomasi and Takeo Kanade. Detection and Tracking of Point Features. 
% Carnegie Mellon University Technical Report CMU-CS-91-132, 1991.
% 
% Jianbo Shi and Carlo Tomasi. Good Features to Track. IEEE Conference on 
% Computer Vision and Pattern Recognition, 1994.
% 
% Zdenek Kalal, Krystian Mikolajczyk and Jiri Matas. Forward-Backward Error: 
% Automatic Detection of Tracking Failures. International Conference on Pattern 
% Recognition, 2010
% 
% _Copyright 2014 The MathWorks, Inc._