Example Background Moving Camera

• 

  Still from a traffic camera video.

Example of background modeling/motion detection from a stationary camera. Moving objects are detected inside the video based on their difference from a background model. These techniques can run very fast (basic runs over 2,000 fps) and be very effective in tracking algorithms

Example File:

• ExampleBackgroundRemovalStationary.java

Concepts:

• Motion Detection
• 2D Image Stitching

Related Examples:

• Background Moving Camera

Example Code

/**
 * Example showing how to perform background modeling when the camera is assumed to be stationary.  This scenario
 * can be computed much faster than the moving camera case and depending on the background model can some times produce
 * reasonable results when the camera has a little bit of jitter.
 *
 * @author Peter Abeles
 */
public class ExampleBackgroundRemovalStationary {
	public static void main(String[] args) {

		String fileName = "../data/applet/background/street_intersection.mp4";
//		String fileName = "../data/applet/background/horse_jitter.mp4"; // degraded performance because of jitter
//		String fileName = "../data/applet/tracking/chipmunk.mjpeg"; // Camera moves.  Stationary will fail here

		// Comment/Uncomment to switch input image type
		ImageType imageType = ImageType.single(ImageFloat32.class);
//		ImageType imageType = ImageType.il(3, InterleavedF32.class);
//		ImageType imageType = ImageType.il(3, InterleavedU8.class);

		// 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(12,0.005f);
		configGaussian.initialVariance = 100;
		configGaussian.minimumDifference = 10;

		// Comment/Uncomment to switch algorithms
		BackgroundModelStationary background =
				FactoryBackgroundModel.stationaryBasic(new ConfigBackgroundBasic(35, 0.005f), imageType);
//				FactoryBackgroundModel.stationaryGaussian(configGaussian, imageType);

		MediaManager media = DefaultMediaManager.INSTANCE;
		SimpleImageSequence video = media.openVideo(fileName, background.getImageType());

		// Declare storage for segmented image.  1 = moving foreground and 0 = background
		ImageUInt8 segmented = new ImageUInt8(video.getNextWidth(),video.getNextHeight());

		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, "Static Scene: Background Segmentation", true);

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

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

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

			try {Thread.sleep(5);} catch (InterruptedException e) {}
		}
		System.out.println("done!");
	}
}