.m文件源码：

%% 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(TM) 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();

% Read a video frame and run the face detector.

videoFileReader = vision.VideoFileReader('tilted_face.avi');

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.

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