boofcv.alg.tracker.circulant

Class CirculantTracker<T extends ImageGray>

java.lang.Object

boofcv.alg.tracker.circulant.CirculantTracker<T>

public class CirculantTracker<T extends ImageGray>
extends java.lang.Object

Tracker that uses the theory of Circulant matrices, Discrete Fourier Transform (DCF), and linear classifiers to track a target and learn its changes in appearance [1]. The target is assumed to be rectangular and has fixed size and location. A dense local search is performed around the most recent target location. The search is done quickly using the DCF.

Tracking is performed using texture information. Since only one description of the target is saved, tracks can drift over time. Tracking performance seems to improve if the object has distinctive edges.

CHANGES FROM PAPER:

• Input image is sampled into a square work region of constant size to improve runtime speed of FFT.
• Peak of response is found using mean-shift. Provides sub-pixel precision.
• Pixels outside the image are assigned random values to avoid the tracker from fitting to them. Ideally they wouldn't be processed, but that is complex to implement

[1] Henriques, Joao F., et al. "Exploiting the circulant structure of tracking-by-detection with kernels." Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 702-715.

Constructor Summary

Constructors

Constructor and Description
CirculantTracker(double output_sigma_factor, double sigma, double lambda, double interp_factor, double padding, int workRegionSize, double maxPixelValue, InterpolatePixelS<T> interp) Configure tracker

Method Summary

Modifier and Type	Method and Description
static void	circshift(GrayF64 a, GrayF64 b)
void	dense_gauss_kernel(double sigma, GrayF64 x, GrayF64 y, GrayF64 k) Gaussian Kernel with dense sampling.
static void	elementMultConjB(InterleavedF64 a, InterleavedF64 b, InterleavedF64 output) Element-wise multiplication of 'a' and the complex conjugate of 'b'
GrayF64	getResponse()
RectangleLength2D_F32	getTargetLocation() The location of the target in the image
GrayF64	getTargetTemplate() Visual appearance of the target
static double	imageDotProduct(GrayF64 a) Computes the dot product of the image with itself
void	initialize(T image, int x0, int y0, int regionWidth, int regionHeight) Initializes tracking around the specified rectangle region
void	performLearning(T image) Update the alphas and the track's appearance
void	performTracking(T image) Search for the track in the image and

Methods inherited from class java.lang.Object

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

Constructor Detail

CirculantTracker

public CirculantTracker(double output_sigma_factor,
                        double sigma,
                        double lambda,
                        double interp_factor,
                        double padding,
                        int workRegionSize,
                        double maxPixelValue,
                        InterpolatePixelS<T> interp)

Configure tracker

Parameters:

output_sigma_factor - spatial bandwidth (proportional to target) Try 1.0/16.0

sigma - Sigma for Gaussian kernel in linear classifier. Try 0.2

lambda - Try 1e-2

interp_factor - Try 0.075

padding - Padding added around the selected target. Try 1

workRegionSize - Size of work region. Best if power of 2. Try 64

maxPixelValue - Maximum pixel value. Typically 255

Method Detail

initialize

public void initialize(T image,
                       int x0,
                       int y0,
                       int regionWidth,
                       int regionHeight)

Initializes tracking around the specified rectangle region

Parameters:

image - Image to start tracking from

x0 - top-left corner of region

y0 - top-left corner of region

regionWidth - region's width

regionHeight - region's height

performTracking

public void performTracking(T image)

Search for the track in the image and

Parameters:

image - Next image in the sequence

performLearning

public void performLearning(T image)

Update the alphas and the track's appearance

dense_gauss_kernel

public void dense_gauss_kernel(double sigma,
                               GrayF64 x,
                               GrayF64 y,
                               GrayF64 k)

Gaussian Kernel with dense sampling. Evaluates a gaussian kernel with bandwidth SIGMA for all displacements between input images X and Y, which must both be MxN. They must also be periodic (ie., pre-processed with a cosine window). The result is an MxN map of responses.

Parameters:

sigma - Gaussian kernel bandwidth

x - Input image

y - Input image

k - Output containing Gaussian kernel for each element in target region

circshift

public static void circshift(GrayF64 a,
                             GrayF64 b)

imageDotProduct

public static double imageDotProduct(GrayF64 a)

Computes the dot product of the image with itself

elementMultConjB

public static void elementMultConjB(InterleavedF64 a,
                                    InterleavedF64 b,
                                    InterleavedF64 output)

Element-wise multiplication of 'a' and the complex conjugate of 'b'

getTargetLocation

public RectangleLength2D_F32 getTargetLocation()

The location of the target in the image

getTargetTemplate

public GrayF64 getTargetTemplate()

Visual appearance of the target

getResponse

public GrayF64 getResponse()