Документация

tracker = multiObjectTracker(...
    'FilterInitializationFcn', @initDemoFilter, ...
    'AssignmentThreshold', 30, ...
    'NumCoastingUpdates', 22, ...
    'ConfirmationParameters', [6 10] ...
    );

    function filter = initDemoFilter(detection)
    % Initialize a Kalman filter for this example.

    % Define the initial state.
    state = [detection.Measurement(1); 0; detection.Measurement(2); 0];

    % Define the initial state covariance.
    stateCov = diag([50, 50, 50, 50]);

    % Create the tracking filter.
    filter = trackingKF('MotionModel', '2D Constant Velocity', ...
        'State', state, ...
        'StateCovariance', stateCov, ...
        'MeasurementNoise', detection.MeasurementNoise(1:2,1:2) ...
        );
    end

% Count frames to create a sense of time.
frameCount = 0;
while hasFrame(videoObjects.reader)
    % Read a video frame and detect objects in it.
    frameCount = frameCount + 1;                                % Promote frame count
    frame = readFrame(videoObjects.reader);                     % Read frame
    [detections, mask] = detectObjects(detectorObjects, frame); % Detect objects in video frame

    % Run the tracker on the preprocessed detections.
    confirmedTracks = updateTracks(tracker, detections, frameCount);

    % Display the tracking results on the video.
    displayTrackingResults(videoObjects, confirmedTracks, frame, mask);
end

    function videoObjects = setupVideoObjects(filename)
        % Initialize video I/O
        % Create objects for reading a video from a file, drawing the tracked
        % objects in each frame, and playing the video.

        % Create a video file reader.
        videoObjects.reader = VideoReader(filename);

        % Create two video players: one to display the video,
        % and one to display the foreground mask.
        videoObjects.maskPlayer  = vision.VideoPlayer('Position', [20, 400, 700, 400]);
        videoObjects.videoPlayer = vision.VideoPlayer('Position', [740, 400, 700, 400]);
    end

    function detectorObjects = setupDetectorObjects(minBlobArea)
        % Create System objects for foreground detection and blob analysis

        % The foreground detector segments moving objects from the
        % background. It outputs a binary mask, where the pixel value of 1
        % corresponds to the foreground and the value of 0 corresponds to
        % the background.

        detectorObjects.detector = vision.ForegroundDetector('NumGaussians', 3, ...
            'NumTrainingFrames', 40, 'MinimumBackgroundRatio', 0.7);

        % Connected groups of foreground pixels are likely to correspond to
        % moving objects.  The blob analysis System object finds such
        % groups (called 'blobs' or 'connected components') and computes
        % their characteristics, such as their areas, centroids, and the
        % bounding boxes.

        detectorObjects.blobAnalyzer = vision.BlobAnalysis('BoundingBoxOutputPort', true, ...
            'AreaOutputPort', true, 'CentroidOutputPort', true, ...
            'MinimumBlobArea', minBlobArea);
    end

    function [detections, mask] = detectObjects(detectorObjects, frame)
        % Expected uncertainty (noise) for the blob centroid.
        measurementNoise = 100*eye(2);
        % Detect foreground.
        mask = detectorObjects.detector.step(frame);

        % Apply morphological operations to remove noise and fill in holes.
        mask = imopen(mask, strel('rectangle', [6, 6]));
        mask = imclose(mask, strel('rectangle', [50, 50]));
        mask = imfill(mask, 'holes');

        % Perform blob analysis to find connected components.
        [~, centroids, bboxes] = detectorObjects.blobAnalyzer.step(mask);

        % Formulate the detections as a list of objectDetection objects.
        numDetections = size(centroids, 1);
        detections = cell(numDetections, 1);
        for i = 1:numDetections
            detections{i} = objectDetection(frameCount, centroids(i,:), ...
                'MeasurementNoise', measurementNoise, ...
                'ObjectAttributes', {bboxes(i,:)});
        end
    end

    function displayTrackingResults(videoObjects, confirmedTracks, frame, mask)
        % Convert the frame and the mask to uint8 RGB.
        frame = im2uint8(frame);
        mask = uint8(repmat(mask, [1, 1, 3])) .* 255;

        if ~isempty(confirmedTracks)
            % Display the objects. If an object has not been detected
            % in this frame, display its predicted bounding box.
            numRelTr = numel(confirmedTracks);
            boxes = zeros(numRelTr, 4);
            ids = zeros(numRelTr, 1, 'int32');
            predictedTrackInds = zeros(numRelTr, 1);
            for tr = 1:numRelTr
                % Get bounding boxes.
                boxes(tr, :) = confirmedTracks(tr).ObjectAttributes{1}{1};

                % Get IDs.
                ids(tr) = confirmedTracks(tr).TrackID;

                if confirmedTracks(tr).IsCoasted
                    predictedTrackInds(tr) = tr;
                end
            end

            predictedTrackInds = predictedTrackInds(predictedTrackInds > 0);

            % Create labels for objects that display the predicted rather
            % than the actual location.
            labels = cellstr(int2str(ids));

            isPredicted = cell(size(labels));
            isPredicted(predictedTrackInds) = {' predicted'};
            labels = strcat(labels, isPredicted);

            % Draw the objects on the frame.
            frame = insertObjectAnnotation(frame, 'rectangle', boxes, labels);

            % Draw the objects on the mask.
            mask = insertObjectAnnotation(mask, 'rectangle', boxes, labels);
        end

        % Display the mask and the frame.
        videoObjects.maskPlayer.step(mask);
        videoObjects.videoPlayer.step(frame);
    end

end