Face Detection and Tracking Using the KLT Algorithm

Face Detection and Tracking Using the KLT Algorithm

from: https://cn.mathworks.com/help/vision/examples/face-detection-and-tracking-using-the-klt-algorithm.html

function [outfilename] = face_det_klt(infilename)
% 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:
% 1.Detect a face
% 2.Identify facial features to track
% 3.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.

addpath(genpath('.'));
infilename='cdd.avi'

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

% Read a video frame and run the face detector.
videoFileReader = vision.VideoFileReader(infilename);
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

end