How to resolve this issue"$octave -qf --no-window-system demo.m warning: function ./demo.m shadows a core library function warning: function name 'MotionBas​edMultiObj​ectTrackin​gExample' does not agree with function filename '/home/cg/​root/31465​60/dem

function multiObjectTracking() % Create system objects used for reading video, detecting moving objects, % and displaying the results. obj = setupSystemObjects();

tracks = initializeTracks(); % Create an empty array of tracks.

nextId = 1; % ID of the next track % Detect moving objects, and track them across video frames. while ~isDone(obj.reader) frame = readFrame(); [centroids, bboxes, mask] = detectObjects(frame); predictNewLocationsOfTracks(); [assignments, unassignedTracks, unassignedDetections] = ... detectionToTrackAssignment(); updateAssignedTracks(); updateUnassignedTracks(); deleteLostTracks(); createNewTracks(); displayTrackingResults(); end

%% Create System Objects % Create System objects used for reading the video frames, detecting % foreground objects, and displaying results.

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

        *obj.reader = vision.VideoFileReader('videonew2.mp4');*

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

        % Create system objects for foreground detection and blob analysis

        % The foreground detector is used to segment 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. 

        obj.detector = vision.ForegroundDetector('NumGaussians', 3, ...
            'NumTrainingFrames', 1, 'MinimumBackgroundRatio', 0.5);
        % Connected groups of foreground pixels are likely to correspond to moving
        % objects.  The blob analysis system object is used to find such groups
        % (called 'blobs' or 'connected components'), and compute their
        % characteristics, such as area, centroid, and the bounding box.

        obj.blobAnalyser = vision.BlobAnalysis('BoundingBoxOutputPort', true, ...
            'AreaOutputPort', true, 'CentroidOutputPort', true, ...
            'MinimumBlobArea', 400);
    end

%% Initialize Tracks

    function tracks = initializeTracks()
        % create an empty array of tracks
        tracks = struct(...
            'id', {}, ...
            'bbox', {}, ...
            'kalmanFilter', {}, ...
            'age', {}, ...
            'totalVisibleCount', {}, ...
            'consecutiveInvisibleCount', {});
    end

%% Read a Video Frame function frame = readFrame() frame = obj.reader.step();

    end

%% Detect Objects function [centroids, bboxes, mask] = detectObjects(frame)

        % Detect foreground.
        mask = obj.detector.step(frame);

        % Apply morphological operations to remove noise and fill in holes.
        mask = imopen(mask, strel('rectangle', [3,3]));
        mask = imclose(mask, strel('rectangle', [15, 15])); 
        mask = imfill(mask, 'holes');

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

%% Predict New Locations of Existing Tracks % Use the Kalman filter to predict the centroid of each track in the % current frame, and update its bounding box accordingly.

    function predictNewLocationsOfTracks()
        for i = 1:length(tracks)
            bbox = tracks(i).bbox;

            % Predict the current location of the track.
            predictedCentroid = predict(tracks(i).kalmanFilter);

            % Shift the bounding box so that its center is at 
            % the predicted location.
            predictedCentroid = int32(predictedCentroid) - bbox(3:4) / 2;
            tracks(i).bbox = [predictedCentroid, bbox(3:4)];
        end
    end

%% Assign Detections to Tracks function [assignments, unassignedTracks, unassignedDetections] = ... detectionToTrackAssignment()

        nTracks = length(tracks);
        nDetections = size(centroids, 1);

        % Compute the cost of assigning each detection to each track.
        cost = zeros(nTracks, nDetections);
        for i = 1:nTracks
            cost(i, :) = distance(tracks(i).kalmanFilter, centroids);
        end

        % Solve the assignment problem.
        costOfNonAssignment = 20;
        [assignments, unassignedTracks, unassignedDetections] = ...
            assignDetectionsToTracks(cost, costOfNonAssignment);
    end

%% Update Assigned Tracks function updateAssignedTracks() numAssignedTracks = size(assignments, 1); for i = 1:numAssignedTracks trackIdx = assignments(i, 1); detectionIdx = assignments(i, 2); centroid = centroids(detectionIdx, :); bbox = bboxes(detectionIdx, :);

            % Correct the estimate of the object's location
            % using the new detection.
            correct(tracks(trackIdx).kalmanFilter, centroid);

            % Replace predicted bounding box with detected
            % bounding box.
            tracks(trackIdx).bbox = bbox;

            % Update track's age.
            tracks(trackIdx).age = tracks(trackIdx).age + 1;

            % Update visibility.
            tracks(trackIdx).totalVisibleCount = ...
                tracks(trackIdx).totalVisibleCount + 1;
            tracks(trackIdx).consecutiveInvisibleCount = 0;
        end
    end

%% Update Unassigned Tracks function updateUnassignedTracks() for i = 1:length(unassignedTracks) ind = unassignedTracks(i); tracks(ind).age = tracks(ind).age + 1; tracks(ind).consecutiveInvisibleCount = ... tracks(ind).consecutiveInvisibleCount + 1; end end

%% Delete Lost Tracks function deleteLostTracks() if isempty(tracks) return; end

        invisibleForTooLong = 20;
        ageThreshold = 8;

        % Compute the fraction of the track's age for which it was visible.
        ages = [tracks(:).age];
        totalVisibleCounts = [tracks(:).totalVisibleCount];
        visibility = totalVisibleCounts ./ ages;

        % Find the indices of 'lost' tracks.
        lostInds = (ages < ageThreshold & visibility < 1.0) | ...
            [tracks(:).consecutiveInvisibleCount] >= invisibleForTooLong;

        % Delete lost tracks.
        tracks = tracks(~lostInds);
    end

%% Create New Tracks function createNewTracks() centroids = centroids(unassignedDetections, :); bboxes = bboxes(unassignedDetections, :);

        for i = 1:size(centroids, 1)

            centroid = centroids(i,:);
            bbox = bboxes(i, :);

            % Create a Kalman filter object.
            kalmanFilter = configureKalmanFilter('ConstantVelocity', ...
                centroid, [200, 50], [100, 25], 100);

            % Create a new track.
            newTrack = struct(...
                'id', nextId, ...
                'bbox', bbox, ...
                'kalmanFilter', kalmanFilter, ...
                'age', 1, ...
                'totalVisibleCount', 1, ...
                'consecutiveInvisibleCount', 0);

            % Add it to the array of tracks.
            tracks(end + 1) = newTrack;

            % Increment the next id.
            nextId = nextId + 1;
        end
    end

%% Display Tracking Results % The displayTrackingResults function draws a bounding box and label ID % for each track on the video frame and the foreground mask. It then % displays the frame and the mask in their respective video players.

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

        minVisibleCount = 8;
        if ~isempty(tracks)

            % Noisy detections tend to result in short-lived tracks.
            % Only display tracks that have been visible for more than 
            % a minimum number of frames.
            reliableTrackInds = ...
                [tracks(:).totalVisibleCount] > minVisibleCount;
            reliableTracks = tracks(reliableTrackInds);

            % Display the objects. If an object has not been detected
            % in this frame, display its predicted bounding box.
            if ~isempty(reliableTracks)
                % Get bounding boxes.
                bboxes = cat(1, reliableTracks.bbox);

                % Get ids.
                ids = int32([reliableTracks(:).id]);

                % Create labels for objects indicating the ones for 
                % which we display the predicted rather than the actual 
                % location.
                bwlabel = cellstr(int2str(ids'));
                predictedTrackInds = ...
                    [reliableTracks(:).consecutiveInvisibleCount] > 0;
                isPredicted = cell(size(bwlabel));
                isPredicted(predictedTrackInds) = {' predicted'};
                bwlabel = strcat(bwlabel, isPredicted);
                % produce sound
                t=0:1/8000:1;                    % 2 secs @ 10kHz sample rate
      y=chirp(t,0,1,3000);
               sound(y);
               % System Date and time 
dateandtime=clock;
fix(dateandtime)
                % Draw the objects on the frame.

                frame = insertObjectAnnotation(frame, 'rectangle', ...
                    bboxes, bwlabel);
                % Number of objects in a frame
numberofobjects=length(tracks)

                % Draw the objects on the mask.
                mask = insertObjectAnnotation(mask, 'rectangle', ...
                    bboxes, bwlabel);

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

    end
displayEndOfDemoMessage(mfilename)
end