boofcv.alg.tracker.meanshift

Class TrackerMeanShiftLikelihood<T extends ImageBase>

java.lang.Object

boofcv.alg.tracker.meanshift.TrackerMeanShiftLikelihood<T>

public class TrackerMeanShiftLikelihood<T extends ImageBase>
extends java.lang.Object

Mean-shift [1] based tracker which tracks the target inside a likelihood image using a flat rectangular kernel of fixed size. The likelihood for each pixel is computed using SparseImageSample_F32. How that model is computed is not specified by this class, but is often based on color. For sake of efficiency, the likelihood for a pixel is only computed as needed.

This algorithm can run very fast and works well when the target being tracked is visually distinctive from the background and largely composed of one color. It can't handle changes in scale or shape of the target, which does limit its applications.

[1] Yizong Chen, "Mean Shift, Mode Seeking, and Clustering" IEEE Trans. Pattern Analysis and Machine Intelligence, VOL. 17, NO. 8, August 1995

Constructor Summary

Constructors

Constructor and Description
TrackerMeanShiftLikelihood(PixelLikelihood<T> targetModel, int maxIterations, float minFractionDrop) Configures tracker

Method Summary

Modifier and Type	Method and Description
RectangleLength2D_I32	getLocation() Current location of target in the image
void	initialize(T image, RectangleLength2D_I32 initial) Specifies the initial target location so that it can learn its description
boolean	isFailed() If true the tracker has filed
boolean	process(T image) Updates the target's location in the image by performing a mean-shift search.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

TrackerMeanShiftLikelihood

public TrackerMeanShiftLikelihood(PixelLikelihood<T> targetModel,
                                  int maxIterations,
                                  float minFractionDrop)

Configures tracker

Parameters:

targetModel - Target used to model the target's likelihood

maxIterations - Maximum number of iterations. try 20

minFractionDrop - If the likelihood drops below its initial value by this fraction the track is assumed to be lost

Method Detail

initialize

public void initialize(T image,
                       RectangleLength2D_I32 initial)

Specifies the initial target location so that it can learn its description

Parameters:

image - Image

initial - Initial target location and the mean-shift bandwidth

process

public boolean process(T image)

Updates the target's location in the image by performing a mean-shift search. Returns if it was successful at finding the target or not. If it fails once it will need to be re-initialized

Parameters:

image - Most recent image in the sequence

Returns:

true for success or false if it failed

getLocation

public RectangleLength2D_I32 getLocation()

Current location of target in the image

Returns:

rectangle containing the target

isFailed

public boolean isFailed()

If true the tracker has filed