This sampler is based on particle filtering. More...

#include <opencv2/tracking/tracking_internals.hpp>

Collaboration diagram for cv::detail::tracking::TrackerSamplerPF:

Classes
struct	Params
	This structure contains all the parameters that can be varied during the course of sampling algorithm. Below is the structure exposed, together with its members briefly explained with reference to the above discussion on algorithm's working. More...

Public Member Functions
	TrackerSamplerPF (const Mat &chosenRect, const TrackerSamplerPF::Params &parameters=TrackerSamplerPF::Params())
	Constructor.

Public Member Functions inherited from cv::detail::tracking::TrackerContribSamplerAlgorithm
virtual	~TrackerContribSamplerAlgorithm ()
	Destructor.

String	getClassName () const
	Get the name of the specific TrackerContribSamplerAlgorithm.

virtual bool	sampling (const Mat &image, const Rect &boundingBox, std::vector< Mat > &sample) CV_OVERRIDE
	Computes the regions starting from a position in an image.

Public Member Functions inherited from cv::detail::tracking::TrackerSamplerAlgorithm
virtual	~TrackerSamplerAlgorithm ()

Protected Member Functions
bool	samplingImpl (const Mat &image, Rect boundingBox, std::vector< Mat > &sample) CV_OVERRIDE

Additional Inherited Members
Static Public Member Functions inherited from cv::detail::tracking::TrackerContribSamplerAlgorithm
static Ptr< TrackerContribSamplerAlgorithm >	create (const String &trackerSamplerType)
	Create TrackerContribSamplerAlgorithm by tracker sampler type.

Protected Attributes inherited from cv::detail::tracking::TrackerContribSamplerAlgorithm
String	className

Detailed Description

This sampler is based on particle filtering.

In principle, it can be thought of as performing some sort of optimization (and indeed, this tracker uses opencv's optim module), where tracker seeks to find the rectangle in given frame, which is the most *"similar"* to the initial rectangle (the one, given through the constructor).

The optimization performed is stochastic and somehow resembles genetic algorithms, where on each new image received (submitted via TrackerSamplerPF::sampling()) we start with the region bounded by boundingBox, then generate several "perturbed" boxes, take the ones most similar to the original. This selection round is repeated several times. At the end, we hope that only the most promising box remaining, and these are combined to produce the subrectangle of image, which is put as a sole element in array sample.

It should be noted, that the definition of "similarity" between two rectangles is based on comparing their histograms. As experiments show, tracker is not very succesfull if target is assumed to strongly change its dimensions.

Constructor & Destructor Documentation

◆ TrackerSamplerPF()

cv::detail::tracking::TrackerSamplerPF::TrackerSamplerPF	(	const Mat &	chosenRect,
		const TrackerSamplerPF::Params &	parameters = `TrackerSamplerPF::Params()`
	)