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Multiple Object Tracking Tutorial

This example shows how to perform automatic detection and motion-based tracking of moving objects in a video. It simplifies the example Motion-Based Multiple Object Tracking (Computer Vision Toolbox) and uses the multiObjectTracker available in Automated Driving Toolbox™.

Detection of moving objects and motion-based tracking are important components of many computer vision applications, including activity recognition, traffic monitoring, and automotive safety. The problem of motion-based object tracking can be divided into two parts:

  1. Detecting moving objects in each frame

  2. Tracking the moving objects from frame to frame

The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. Morphological operations are applied to the resulting foreground mask to eliminate noise. Finally, blob analysis detects groups of connected pixels, which are likely to correspond to moving objects.

The tracking of moving objects from frame to frame is done by the multiObjectTracker object that is responsible for the following:

  1. Assigning detections to tracks.

  2. Initializing new tracks based on unassigned detections. All tracks are initialized as 'Tentative', accounting for the possibility that they resulted from a false detection.

  3. Confirming tracks if they have more than M assigned detections in N frames.

  4. Updating existing tracks based on assigned detections.

  5. Coasting (predicting) existing unassigned tracks.

  6. Deleting tracks if they have remained unassigned (coasted) for too long.

The assignment of detections to the same object is based solely on motion. The motion of each track is estimated by a Kalman filter. The filter predicts the track's location in each frame, and determines the likelihood of each detection being assigned to each track. To initialize the filter that you design, use the FilterInitializationFcn property of the multiObjectTracker.

For more information, see Multiple Object Tracking (Computer Vision Toolbox).

This example is a function, with the main body at the top and helper routines in the form of nested functions below. See Nested Functions (MATLAB) for more details.

function MultipleObjectTrackingExample()

% Create objects used for reading video and displaying the results.
videoObjects = setupVideoObjects('atrium.mp4');

% Create objects used for detecting objects in the foreground of the video.
minBlobArea = 400; % Minimum blob size, in pixels, to be considered as a detection
detectorObjects = setupDetectorObjects(minBlobArea);

Create the Multi-Object Tracker

When creating a multiObjectTracker, consider the following:

  1. FilterInitializationFcn: The likely motion and measurement models. In this case, the objects are expected to have a constant speed motion. The initDemoFilter function configures a linear Kalman filter to track the motion. See the 'Define a Kalman filter' section for details.

  2. AssignmentThreshold: How far detections may fall from tracks. The default value for this parameter is 30. If there are detections that are not assigned to tracks, but should be, increase this value. If there are detections that get assigned to tracks that are too far, decrease this value.

  3. NumCoastingUpdates: How long a track is maintained before deletion. In this case, since the video has 30 frames per second, a reasonable value is about 0.75 seconds (22 frames).

  4. ConfirmationParameters: The parameters controlling track confirmation. A track is initialized with every unassigned detection. Some of these detections might be false, so initially, all tracks are 'Tentative'. To confirm a track, it has to be detected at least M out of N frames. The choice of M and N depends on the visibility of the objects. This example assumes a visibility of 6 out of 10 frames.

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

Define a Kalman Filter

When defining a tracking filter for the motion, complete the following steps:

Step 1: Define the motion model and state

In this example, use a constant velocity model in a 2-D rectangular frame.

  1. The state is [x;vx;y;vy].

  2. The state transition model matrix is A = [1 dt 0 0; 0 1 0 0; 0 0 1 dt; 0 0 0 1].

  3. Assume that dt = 1.

Step 2: Define the process noise

The process noise represents the parts of the process that are not taken into account in the model. For example, in a constant velocity model, the acceleration is neglected.

Step 3: Define the measurement model

In this example, only the position ([x;y]) is measured. So, the measurement model is H = [1 0 0 0; 0 0 1 0].

Note: To preconfigure these parameters, define the 'MotionModel' property as '2D Constant Velocity'.

Step 4: Initialize the state vector based on the sensor measurement

In this example, because the measurement is [x;y] and the state is [x;vx;y;vy], initializing the state vector is straightforward. Because there is no measurement of the velocity, initialize the vx and vy components to 0.

Step 5: Define an initial state covariance

In this example, the measurements are quite noisy, so define the initial state covariance to be quite large: stateCov = diag([50, 50, 50, 50])

Step 6: Create the correct filter

In this example, all the models are linear, so use trackingKF as the tracking filter.

    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

The following loop runs the video clip, detects moving objects in the video, and tracks them across video frames.

% 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

Create Video Objects

Create objects used for reading and displaying the video frames.

    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

Create Detector Objects

Create objects used for detecting foreground objects. Use minBlobArea to define the size of the blob, in pixels, that is considered to be a detection.

  • Increase minBlobArea to avoid detecting small blobs, which are more likely to be false detections, or if several detections are created for the same object due to partial occlusion.

  • Decrease minBlobArea if objects are detected too late or not at all.

    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

Detect Objects

The detectObjects function returns the centroids and the bounding boxes of the detected objects as a list of objectDetection objects. You can supply this list as an input to the multiObjectTracker. The detectObjects function also returns the binary mask, which has the same size as the input frame. Pixels with a value of 1 correspond to the foreground. Pixels with a value of 0 correspond to the background.

The function performs motion segmentation using the foreground detector. It then performs morphological operations on the resulting binary mask to remove noisy pixels and to fill the holes in the remaining blobs.

When creating the objectDetection list, the frameCount serves as the time input, and the centroids of the detected blobs serve as the measurement. The list also has two optional name-value pairs:

  • MeasurementNoise - Blob detection is noisy, and this example defines a large measurement noise value.

  • ObjectAttributes - The detected bounding boxes that get passed to the track display are added to this argument.

    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

Display Tracking Results

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

    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

Summary

In this example, you created a motion-based system for detecting and tracking multiple moving objects. Try using a different video to see if you can detect and track objects. Try modifying the parameters of the multiObjectTracker.

The tracking in this example was based solely on motion, with the assumption that all objects move in a straight line with constant speed. When the motion of an object significantly deviates from this model, the example can produce tracking errors. Notice the mistake in tracking the person occluded by the tree.

You can reduce the likelihood of tracking errors by using a more complex motion model, such as constant acceleration or constant turn. To do that, try defining a different tracking filter, such as trackingEKF or trackingUKF.

See Also

Objects

Related Topics