Hi,
I am currently using Matlab R2013a and am running the code as stated below. Was trying to test out the codes but received an error message : Undefined function 'bbox2points' for input arguments of type 'double'.
I am not sure what this message is as i am new to matlab.
Really appreciate your help.
%% 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. % % Copyright 2014 The MathWorks, Inc.
%% 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();
% 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.
%% 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
displayEndOfDemoMessage(mfilename)
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