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 framesoldPoints = 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 uprelease(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, 2010end