View source for Example Background Moving Camera

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<gallery heights=200 widths=500 >
Image:Background_moving_camera.jpg|Still from a video where a camera is following a chipmunk walk across a stone path.
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In this example the objects which are moving relative to the background are highlighted in a binary image.  While much slower than background modeling for static cameras, it can handle gradual camera motion when viewing environments that are approximately planar or being viewed for a distance.  If the same videos were feed into a static camera background motion the entire image would be lit up as moving. 

Example File: 
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/tracking/ExampleBackgroundRemovalMoving.java ExampleBackgroundRemovalMoving.java]

Concepts:
* Motion Detection
* 2D Image Stitching

Related Examples:
* [[Example_Background_Stationary_Camera| Background Stationary Camera]]

= Example Code =

<syntaxhighlight lang="java">
/**
 * Example showing how to perform background modeling with a moving camera. Here the camera's motion is explicitly
 * estimated using a motion model. That motion model is then used to distort the image and generate background.
 * The net affect is a significant reduction in false positives around the objects of images in oscillating cameras
 * and the ability to detect motion in moving scenes.
 *
 * @author Peter Abeles
 */
public class ExampleBackgroundRemovalMoving {
	public static void main( String[] args ) {
		// Example with a moving camera. Highlights why motion estimation is sometimes required
		String fileName = UtilIO.pathExample("tracking/chipmunk.mjpeg");
		// Camera has a bit of jitter in it. Static kinda works but motion reduces false positives
//		String fileName = UtilIO.pathExample("background/horse_jitter.mp4");

		// Comment/Uncomment to switch input image type
		ImageType imageType = ImageType.single(GrayF32.class);
//		ImageType imageType = ImageType.il(3, InterleavedF32.class);
//		ImageType imageType = ImageType.il(3, InterleavedU8.class);

		// Configure the feature detector
		ConfigPointDetector configDetector = new ConfigPointDetector();
		configDetector.type = PointDetectorTypes.SHI_TOMASI;
		configDetector.general.maxFeatures = 300;
		configDetector.general.radius = 6;
		configDetector.general.threshold = 10;

		// Use a KLT tracker
		PointTracker tracker = FactoryPointTracker.klt(4, configDetector, 3, GrayF32.class, null);

		// This estimates the 2D image motion
		ImageMotion2D<GrayF32, Homography2D_F64> motion2D =
				FactoryMotion2D.createMotion2D(500, 0.5, 3, 100, 0.6, 0.5, false, tracker, new Homography2D_F64());

		ConfigBackgroundBasic configBasic = new ConfigBackgroundBasic(30, 0.005f);

		// Configuration for Gaussian model. Note that the threshold changes depending on the number of image bands
		// 12 = gray scale and 40 = color
		ConfigBackgroundGaussian configGaussian = new ConfigBackgroundGaussian(12, 0.001f);
		configGaussian.initialVariance = 64;
		configGaussian.minimumDifference = 5;

		// Note that GMM doesn't interpolate the input image. Making it harder to model object edges.
		// However it runs faster because of this.
		ConfigBackgroundGmm configGmm = new ConfigBackgroundGmm();
		configGmm.initialVariance = 1600;
		configGmm.significantWeight = 1e-1f;

		// Comment/Uncomment to switch background mode
		BackgroundModelMoving background =
				FactoryBackgroundModel.movingBasic(configBasic, new PointTransformHomography_F32(), imageType);
//				FactoryBackgroundModel.movingGaussian(configGaussian, new PointTransformHomography_F32(), imageType);
//				FactoryBackgroundModel.movingGmm(configGmm,new PointTransformHomography_F32(), imageType);

		background.setUnknownValue(1);

		MediaManager media = DefaultMediaManager.INSTANCE;
		SimpleImageSequence video =
				media.openVideo(fileName, background.getImageType());
//				media.openCamera(null,640,480,background.getImageType());

		//====== Initialize Images

		// storage for segmented image. Background = 0, Foreground = 1
		GrayU8 segmented = new GrayU8(video.getWidth(), video.getHeight());
		// Grey scale image that's the input for motion estimation
		GrayF32 grey = new GrayF32(segmented.width, segmented.height);

		// coordinate frames
		Homography2D_F32 firstToCurrent32 = new Homography2D_F32();
		Homography2D_F32 homeToWorld = new Homography2D_F32();
		homeToWorld.a13 = grey.width/2;
		homeToWorld.a23 = grey.height/2;

		// Create a background image twice the size of the input image. Tell it that the home is in the center
		background.initialize(grey.width*2, grey.height*2, homeToWorld);

		BufferedImage visualized = new BufferedImage(segmented.width, segmented.height, BufferedImage.TYPE_INT_RGB);
		ImageGridPanel gui = new ImageGridPanel(1, 2);
		gui.setImages(visualized, visualized);

		ShowImages.showWindow(gui, "Detections", true);

		double fps = 0;
		double alpha = 0.01; // smoothing factor for FPS

		while (video.hasNext()) {
			ImageBase input = video.next();

			long before = System.nanoTime();
			GConvertImage.convert(input, grey);

			if (!motion2D.process(grey)) {
				throw new RuntimeException("Should handle this scenario");
			}

			Homography2D_F64 firstToCurrent64 = motion2D.getFirstToCurrent();
			ConvertMatrixData.convert(firstToCurrent64, firstToCurrent32);

			background.segment(firstToCurrent32, input, segmented);
			background.updateBackground(firstToCurrent32, input);
			long after = System.nanoTime();

			fps = (1.0 - alpha)*fps + alpha*(1.0/((after - before)/1e9));

			VisualizeBinaryData.renderBinary(segmented, false, visualized);
			gui.setImage(0, 0, (BufferedImage)video.getGuiImage());
			gui.setImage(0, 1, visualized);
			gui.repaint();

			System.out.println("FPS = " + fps);

			BoofMiscOps.sleep(5);
		}
	}
}
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