Face Detection and Tracking Using the KLT Algorithm
阿新 • • 發佈:2018-11-20
Face Detection and Tracking Using the KLT Algorithm
from: https://cn.mathworks.com/help/vision/examples/face-detection-and-tracking-using-the-klt-algorithm.htmlfunction [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