Difference between revisions of "Example Background Stationary Camera"

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Example File:  
Example File:  
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.23/examples/src/boofcv/examples/tracking/ExampleBackgroundRemovalStationary.java ExampleBackgroundRemovalStationary.java]
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.29/examples/src/boofcv/examples/tracking/ExampleBackgroundRemovalStationary.java ExampleBackgroundRemovalStationary.java]


Concepts:
Concepts:
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String fileName = UtilIO.pathExample("background/street_intersection.mp4");
String fileName = UtilIO.pathExample("background/street_intersection.mp4");
// String fileName = UtilIO.pathExample("background/rubixfire.mp4"); // dynamic background
// String fileName = UtilIO.pathExample("background/horse_jitter.mp4"); // degraded performance because of jitter
// String fileName = UtilIO.pathExample("background/horse_jitter.mp4"); // degraded performance because of jitter
// String fileName = UtilIO.pathExample("tracking/chipmunk.mjpeg"); // Camera moves.  Stationary will fail here
// String fileName = UtilIO.pathExample("tracking/chipmunk.mjpeg"); // Camera moves.  Stationary will fail here
Line 39: Line 40:
// ImageType imageType = ImageType.il(3, InterleavedU8.class);
// ImageType imageType = ImageType.il(3, InterleavedU8.class);


// Configuration for Gaussian model.  Note that the threshold changes depending on the number of image bands
ConfigBackgroundGmm configGmm = new ConfigBackgroundGmm();
// 12 = gray scale and 40 = color
ConfigBackgroundGaussian configGaussian = new ConfigBackgroundGaussian(12,0.0005f);
configGaussian.initialVariance = 100;
configGaussian.minimumDifference = 10;


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


MediaManager media = DefaultMediaManager.INSTANCE;
MediaManager media = DefaultMediaManager.INSTANCE;
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long before = System.nanoTime();
long before = System.nanoTime();
background.segment(input,segmented);
background.updateBackground(input,segmented);
background.updateBackground(input);
long after = System.nanoTime();
long after = System.nanoTime();



Revision as of 19:10, 18 March 2018

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:

Concepts:

  • Motion Detection
  • 2D Image Stitching

Related Examples:

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 = UtilIO.pathExample("background/street_intersection.mp4");
//		String fileName = UtilIO.pathExample("background/rubixfire.mp4"); // dynamic background
//		String fileName = UtilIO.pathExample("background/horse_jitter.mp4"); // degraded performance because of jitter
//		String fileName = UtilIO.pathExample("tracking/chipmunk.mjpeg"); // Camera moves.  Stationary will fail here

		// 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);

		ConfigBackgroundGmm configGmm = new ConfigBackgroundGmm();

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

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

		// Declare storage for segmented image.  1 = moving foreground and 0 = background
		GrayU8 segmented = new GrayU8(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.updateBackground(input,segmented);
			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!");
	}
}