Example Track Point Features

Tracked point features in an image sequence. Blue dots are older tracks and green dots are newly spawned tracks.	Video Introduction

Tracking how point features move inside an image is used to extract the geometric structure and apparent motion of the scene. There are many different ways in which point features are tracked. BoofCV provides a basic tracker that hides much of this complexity and allows a variety of different trackers to be used with out modifying any of the code.

The example code below shows how to use the ImagePointTracker interface to process images and get a list of detected points. Which tracker is used can be changed by toggling comments in the main function.

Example Code:

• ExamplePointFeatureTracker.java

Concepts:

• Loading image sequences
• Tracking point features abstractly
• Displaying the location of point features

Videos:

• Point Tracker Updates 2020

Example Code

/**
 * <p>
 * Example of how to use the {@link boofcv.abst.tracker.PointTracker} to track different types of point features.
 * ImagePointTracker hides much of the complexity involved in tracking point features and masks
 * the very different underlying structures used by these different trackers. The default trackers
 * provided in BoofCV are general purpose trackers, that might not be the best tracker or utility
 * the underlying image features the best in all situations.
 * </p>
 *
 * @author Peter Abeles
 */
public class ExamplePointFeatureTracker<T extends ImageGray<T>, D extends ImageGray<D>> {
	// type of input image
	Class<T> imageType;
	Class<D> derivType;

	// tracks point features inside the image
	PointTracker<T> tracker;

	// displays the video sequence and tracked features
	ImagePanel gui = new ImagePanel();

	int pause;

	public ExamplePointFeatureTracker( Class<T> imageType, int pause ) {
		this.imageType = imageType;
		this.derivType = GImageDerivativeOps.getDerivativeType(imageType);
		this.pause = pause;
	}

	/**
	 * Processes the sequence of images and displays the tracked features in a window
	 */
	public void process( SimpleImageSequence<T> sequence ) {
		// Figure out how large the GUI window should be
		T frame = sequence.next();
		gui.setPreferredSize(new Dimension(frame.getWidth(), frame.getHeight()));
		ShowImages.showWindow(gui, "KTL Tracker", true);

		// process each frame in the image sequence
		while (sequence.hasNext()) {
			frame = sequence.next();

			// tell the tracker to process the frame
			tracker.process(frame);

			// if there are too few tracks spawn more
			if (tracker.getTotalActive() < 130)
				tracker.spawnTracks();

			// visualize tracking results
			updateGUI(sequence);

			// wait for a fraction of a second so it doesn't process to fast
			BoofMiscOps.pause(pause);
		}
	}

	/**
	 * Draw tracked features in blue, or red if they were just spawned.
	 */
	private void updateGUI( SimpleImageSequence<T> sequence ) {
		BufferedImage orig = sequence.getGuiImage();
		Graphics2D g2 = orig.createGraphics();

		// draw tracks with semi-unique colors so you can track individual points with your eyes
		for (PointTrack p : tracker.getActiveTracks(null)) {
			int red = (int)(2.5*(p.featureId%100));
			int green = (int)((255.0/150.0)*(p.featureId%150));
			int blue = (int)(p.featureId%255);
			VisualizeFeatures.drawPoint(g2, (int)p.pixel.x, (int)p.pixel.y, new Color(red, green, blue));
		}

		// draw tracks which have just been spawned green
		for (PointTrack p : tracker.getNewTracks(null)) {
			VisualizeFeatures.drawPoint(g2, (int)p.pixel.x, (int)p.pixel.y, Color.green);
		}

		// tell the GUI to update
		gui.setImage(orig);
		gui.repaint();
	}

	/**
	 * A simple way to create a Kanade-Lucas-Tomasi (KLT) tracker.
	 */
	public void createKLT() {
		var configKlt = new ConfigPKlt();
		configKlt.templateRadius = 3;
		configKlt.pyramidLevels = ConfigDiscreteLevels.levels(4);

		var configDetector = new ConfigPointDetector();
		configDetector.type = PointDetectorTypes.SHI_TOMASI;
		configDetector.general.maxFeatures = 600;
		configDetector.general.radius = 6;
		configDetector.general.threshold = 1;

		tracker = FactoryPointTracker.klt(configKlt, configDetector, imageType, derivType);
	}

	/**
	 * Creates a SURF feature tracker.
	 */
	public void createSURF() {
		var configDetector = new ConfigFastHessian();
		configDetector.maxFeaturesPerScale = 250;
		configDetector.extract.radius = 3;
		configDetector.initialSampleStep = 2;
		tracker = FactoryPointTracker.dda_FH_SURF_Fast(configDetector, null, null, imageType);
	}

	public static void main( String[] args ) throws FileNotFoundException {
		Class<GrayF32> imageType = GrayF32.class;

		MediaManager media = DefaultMediaManager.INSTANCE;

		int pause;
		SimpleImageSequence<GrayF32> sequence =
				media.openVideo(UtilIO.pathExample("zoom.mjpeg"), ImageType.single(imageType));
		pause = 100;
//				media.openCamera(null,640,480,ImageType.single(imageType)); pause = 5;
		sequence.setLoop(true);

		var app = new ExamplePointFeatureTracker<>(imageType, pause);

		// Comment or un-comment to change the type of tracker being used
		app.createKLT();
//		app.createSURF();

		app.process(sequence);
	}
}