/
tracker.hpp
1450 lines (1151 loc) · 47 KB
/
tracker.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_TRACKER_HPP__
#define __OPENCV_TRACKER_HPP__
#include "opencv2/core.hpp"
#include "opencv2/imgproc/types_c.h"
#include "feature.hpp"
#include "onlineMIL.hpp"
#include "onlineBoosting.hpp"
/*
* Partially based on:
* ====================================================================================================================
* - [AAM] S. Salti, A. Cavallaro, L. Di Stefano, Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation
* - [AMVOT] X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, A. van den Hengel, A Survey of Appearance Models in Visual Object Tracking
*
* This Tracking API has been designed with PlantUML. If you modify this API please change UML files under modules/tracking/doc/uml
*
*/
namespace cv
{
//! @addtogroup tracking
//! @{
/************************************ TrackerFeature Base Classes ************************************/
/** @brief Abstract base class for TrackerFeature that represents the feature.
*/
class CV_EXPORTS TrackerFeature
{
public:
virtual ~TrackerFeature();
/** @brief Compute the features in the images collection
@param images The images
@param response The output response
*/
void compute( const std::vector<Mat>& images, Mat& response );
/** @brief Create TrackerFeature by tracker feature type
@param trackerFeatureType The TrackerFeature name
The modes available now:
- "HAAR" -- Haar Feature-based
The modes that will be available soon:
- "HOG" -- Histogram of Oriented Gradients features
- "LBP" -- Local Binary Pattern features
- "FEATURE2D" -- All types of Feature2D
*/
static Ptr<TrackerFeature> create( const String& trackerFeatureType );
/** @brief Identify most effective features
@param response Collection of response for the specific TrackerFeature
@param npoints Max number of features
@note This method modifies the response parameter
*/
virtual void selection( Mat& response, int npoints ) = 0;
/** @brief Get the name of the specific TrackerFeature
*/
String getClassName() const;
protected:
virtual bool computeImpl( const std::vector<Mat>& images, Mat& response ) = 0;
String className;
};
/** @brief Class that manages the extraction and selection of features
@cite AAM Feature Extraction and Feature Set Refinement (Feature Processing and Feature Selection).
See table I and section III C @cite AMVOT Appearance modelling -\> Visual representation (Table II,
section 3.1 - 3.2)
TrackerFeatureSet is an aggregation of TrackerFeature
@sa
TrackerFeature
*/
class CV_EXPORTS TrackerFeatureSet
{
public:
TrackerFeatureSet();
~TrackerFeatureSet();
/** @brief Extract features from the images collection
@param images The input images
*/
void extraction( const std::vector<Mat>& images );
/** @brief Identify most effective features for all feature types (optional)
*/
void selection();
/** @brief Remove outliers for all feature types (optional)
*/
void removeOutliers();
/** @brief Add TrackerFeature in the collection. Return true if TrackerFeature is added, false otherwise
@param trackerFeatureType The TrackerFeature name
The modes available now:
- "HAAR" -- Haar Feature-based
The modes that will be available soon:
- "HOG" -- Histogram of Oriented Gradients features
- "LBP" -- Local Binary Pattern features
- "FEATURE2D" -- All types of Feature2D
Example TrackerFeatureSet::addTrackerFeature : :
@code
//sample usage:
Ptr<TrackerFeature> trackerFeature = new TrackerFeatureHAAR( HAARparameters );
featureSet->addTrackerFeature( trackerFeature );
//or add CSC sampler with default parameters
//featureSet->addTrackerFeature( "HAAR" );
@endcode
@note If you use the second method, you must initialize the TrackerFeature
*/
bool addTrackerFeature( String trackerFeatureType );
/** @overload
@param feature The TrackerFeature class
*/
bool addTrackerFeature( Ptr<TrackerFeature>& feature );
/** @brief Get the TrackerFeature collection (TrackerFeature name, TrackerFeature pointer)
*/
const std::vector<std::pair<String, Ptr<TrackerFeature> > >& getTrackerFeature() const;
/** @brief Get the responses
@note Be sure to call extraction before getResponses Example TrackerFeatureSet::getResponses : :
*/
const std::vector<Mat>& getResponses() const;
private:
void clearResponses();
bool blockAddTrackerFeature;
std::vector<std::pair<String, Ptr<TrackerFeature> > > features; //list of features
std::vector<Mat> responses; //list of response after compute
};
/************************************ TrackerSampler Base Classes ************************************/
/** @brief Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific
sampler.
*/
class CV_EXPORTS TrackerSamplerAlgorithm
{
public:
/**
* \brief Destructor
*/
virtual ~TrackerSamplerAlgorithm();
/** @brief Create TrackerSamplerAlgorithm by tracker sampler type.
@param trackerSamplerType The trackerSamplerType name
The modes available now:
- "CSC" -- Current State Center
- "CS" -- Current State
*/
static Ptr<TrackerSamplerAlgorithm> create( const String& trackerSamplerType );
/** @brief Computes the regions starting from a position in an image.
Return true if samples are computed, false otherwise
@param image The current frame
@param boundingBox The bounding box from which regions can be calculated
@param sample The computed samples @cite AAM Fig. 1 variable Sk
*/
bool sampling( const Mat& image, Rect boundingBox, std::vector<Mat>& sample );
/** @brief Get the name of the specific TrackerSamplerAlgorithm
*/
String getClassName() const;
protected:
String className;
virtual bool samplingImpl( const Mat& image, Rect boundingBox, std::vector<Mat>& sample ) = 0;
};
/**
* \brief Class that manages the sampler in order to select regions for the update the model of the tracker
* [AAM] Sampling e Labeling. See table I and section III B
*/
/** @brief Class that manages the sampler in order to select regions for the update the model of the tracker
@cite AAM Sampling e Labeling. See table I and section III B
TrackerSampler is an aggregation of TrackerSamplerAlgorithm
@sa
TrackerSamplerAlgorithm
*/
class CV_EXPORTS TrackerSampler
{
public:
/**
* \brief Constructor
*/
TrackerSampler();
/**
* \brief Destructor
*/
~TrackerSampler();
/** @brief Computes the regions starting from a position in an image
@param image The current frame
@param boundingBox The bounding box from which regions can be calculated
*/
void sampling( const Mat& image, Rect boundingBox );
/** @brief Return the collection of the TrackerSamplerAlgorithm
*/
const std::vector<std::pair<String, Ptr<TrackerSamplerAlgorithm> > >& getSamplers() const;
/** @brief Return the samples from all TrackerSamplerAlgorithm, @cite AAM Fig. 1 variable Sk
*/
const std::vector<Mat>& getSamples() const;
/** @brief Add TrackerSamplerAlgorithm in the collection. Return true if sampler is added, false otherwise
@param trackerSamplerAlgorithmType The TrackerSamplerAlgorithm name
The modes available now:
- "CSC" -- Current State Center
- "CS" -- Current State
- "PF" -- Particle Filtering
Example TrackerSamplerAlgorithm::addTrackerSamplerAlgorithm : :
@code
TrackerSamplerCSC::Params CSCparameters;
Ptr<TrackerSamplerAlgorithm> CSCSampler = new TrackerSamplerCSC( CSCparameters );
if( !sampler->addTrackerSamplerAlgorithm( CSCSampler ) )
return false;
//or add CSC sampler with default parameters
//sampler->addTrackerSamplerAlgorithm( "CSC" );
@endcode
@note If you use the second method, you must initialize the TrackerSamplerAlgorithm
*/
bool addTrackerSamplerAlgorithm( String trackerSamplerAlgorithmType );
/** @overload
@param sampler The TrackerSamplerAlgorithm
*/
bool addTrackerSamplerAlgorithm( Ptr<TrackerSamplerAlgorithm>& sampler );
private:
std::vector<std::pair<String, Ptr<TrackerSamplerAlgorithm> > > samplers;
std::vector<Mat> samples;
bool blockAddTrackerSampler;
void clearSamples();
};
/************************************ TrackerModel Base Classes ************************************/
/** @brief Abstract base class for TrackerTargetState that represents a possible state of the target.
See @cite AAM \f$\hat{x}^{i}_{k}\f$ all the states candidates.
Inherits this class with your Target state, In own implementation you can add scale variation,
width, height, orientation, etc.
*/
class CV_EXPORTS TrackerTargetState
{
public:
virtual ~TrackerTargetState()
{
}
;
/**
* \brief Get the position
* \return The position
*/
Point2f getTargetPosition() const;
/**
* \brief Set the position
* \param position The position
*/
void setTargetPosition( const Point2f& position );
/**
* \brief Get the width of the target
* \return The width of the target
*/
int getTargetWidth() const;
/**
* \brief Set the width of the target
* \param width The width of the target
*/
void setTargetWidth( int width );
/**
* \brief Get the height of the target
* \return The height of the target
*/
int getTargetHeight() const;
/**
* \brief Set the height of the target
* \param height The height of the target
*/
void setTargetHeight( int height );
protected:
Point2f targetPosition;
int targetWidth;
int targetHeight;
};
/** @brief Represents the model of the target at frame \f$k\f$ (all states and scores)
See @cite AAM The set of the pair \f$\langle \hat{x}^{i}_{k}, C^{i}_{k} \rangle\f$
@sa TrackerTargetState
*/
typedef std::vector<std::pair<Ptr<TrackerTargetState>, float> > ConfidenceMap;
/** @brief Represents the estimate states for all frames
@cite AAM \f$x_{k}\f$ is the trajectory of the target up to time \f$k\f$
@sa TrackerTargetState
*/
typedef std::vector<Ptr<TrackerTargetState> > Trajectory;
/** @brief Abstract base class for TrackerStateEstimator that estimates the most likely target state.
See @cite AAM State estimator
See @cite AMVOT Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid)
*/
class CV_EXPORTS TrackerStateEstimator
{
public:
virtual ~TrackerStateEstimator();
/** @brief Estimate the most likely target state, return the estimated state
@param confidenceMaps The overall appearance model as a list of :cConfidenceMap
*/
Ptr<TrackerTargetState> estimate( const std::vector<ConfidenceMap>& confidenceMaps );
/** @brief Update the ConfidenceMap with the scores
@param confidenceMaps The overall appearance model as a list of :cConfidenceMap
*/
void update( std::vector<ConfidenceMap>& confidenceMaps );
/** @brief Create TrackerStateEstimator by tracker state estimator type
@param trackeStateEstimatorType The TrackerStateEstimator name
The modes available now:
- "BOOSTING" -- Boosting-based discriminative appearance models. See @cite AMVOT section 4.4
The modes available soon:
- "SVM" -- SVM-based discriminative appearance models. See @cite AMVOT section 4.5
*/
static Ptr<TrackerStateEstimator> create( const String& trackeStateEstimatorType );
/** @brief Get the name of the specific TrackerStateEstimator
*/
String getClassName() const;
protected:
virtual Ptr<TrackerTargetState> estimateImpl( const std::vector<ConfidenceMap>& confidenceMaps ) = 0;
virtual void updateImpl( std::vector<ConfidenceMap>& confidenceMaps ) = 0;
String className;
};
/** @brief Abstract class that represents the model of the target. It must be instantiated by specialized
tracker
See @cite AAM Ak
Inherits this with your TrackerModel
*/
class CV_EXPORTS TrackerModel
{
public:
/**
* \brief Constructor
*/
TrackerModel();
/**
* \brief Destructor
*/
virtual ~TrackerModel();
/** @brief Set TrackerEstimator, return true if the tracker state estimator is added, false otherwise
@param trackerStateEstimator The TrackerStateEstimator
@note You can add only one TrackerStateEstimator
*/
bool setTrackerStateEstimator( Ptr<TrackerStateEstimator> trackerStateEstimator );
/** @brief Estimate the most likely target location
@cite AAM ME, Model Estimation table I
@param responses Features extracted from TrackerFeatureSet
*/
void modelEstimation( const std::vector<Mat>& responses );
/** @brief Update the model
@cite AAM MU, Model Update table I
*/
void modelUpdate();
/** @brief Run the TrackerStateEstimator, return true if is possible to estimate a new state, false otherwise
*/
bool runStateEstimator();
/** @brief Set the current TrackerTargetState in the Trajectory
@param lastTargetState The current TrackerTargetState
*/
void setLastTargetState( const Ptr<TrackerTargetState>& lastTargetState );
/** @brief Get the last TrackerTargetState from Trajectory
*/
Ptr<TrackerTargetState> getLastTargetState() const;
/** @brief Get the list of the ConfidenceMap
*/
const std::vector<ConfidenceMap>& getConfidenceMaps() const;
/** @brief Get the last ConfidenceMap for the current frame
*/
const ConfidenceMap& getLastConfidenceMap() const;
/** @brief Get the TrackerStateEstimator
*/
Ptr<TrackerStateEstimator> getTrackerStateEstimator() const;
private:
void clearCurrentConfidenceMap();
protected:
std::vector<ConfidenceMap> confidenceMaps;
Ptr<TrackerStateEstimator> stateEstimator;
ConfidenceMap currentConfidenceMap;
Trajectory trajectory;
int maxCMLength;
virtual void modelEstimationImpl( const std::vector<Mat>& responses ) = 0;
virtual void modelUpdateImpl() = 0;
};
/************************************ Tracker Base Class ************************************/
/** @brief Base abstract class for the long-term tracker:
*/
class CV_EXPORTS_W Tracker : public virtual Algorithm
{
public:
virtual ~Tracker();
/** @brief Initialize the tracker with a know bounding box that surrounding the target
@param image The initial frame
@param boundingBox The initial boundig box
@return True if initialization went succesfully, false otherwise
*/
CV_WRAP bool init( InputArray image, const Rect2d& boundingBox );
/** @brief Update the tracker, find the new most likely bounding box for the target
@param image The current frame
@param boundingBox The boundig box that represent the new target location, if true was returned, not
modified otherwise
@return True means that target was located and false means that tracker cannot locate target in
current frame. Note, that latter *does not* imply that tracker has failed, maybe target is indeed
missing from the frame (say, out of sight)
*/
CV_WRAP bool update( InputArray image, CV_OUT Rect2d& boundingBox );
virtual void read( const FileNode& fn )=0;
virtual void write( FileStorage& fs ) const=0;
protected:
virtual bool initImpl( const Mat& image, const Rect2d& boundingBox ) = 0;
virtual bool updateImpl( const Mat& image, Rect2d& boundingBox ) = 0;
bool isInit;
Ptr<TrackerFeatureSet> featureSet;
Ptr<TrackerSampler> sampler;
Ptr<TrackerModel> model;
};
/************************************ Specific TrackerStateEstimator Classes ************************************/
/** @brief TrackerStateEstimator based on Boosting
*/
class CV_EXPORTS TrackerStateEstimatorMILBoosting : public TrackerStateEstimator
{
public:
/**
* Implementation of the target state for TrackerStateEstimatorMILBoosting
*/
class TrackerMILTargetState : public TrackerTargetState
{
public:
/**
* \brief Constructor
* \param position Top left corner of the bounding box
* \param width Width of the bounding box
* \param height Height of the bounding box
* \param foreground label for target or background
* \param features features extracted
*/
TrackerMILTargetState( const Point2f& position, int width, int height, bool foreground, const Mat& features );
/**
* \brief Destructor
*/
~TrackerMILTargetState()
{
}
;
/** @brief Set label: true for target foreground, false for background
@param foreground Label for background/foreground
*/
void setTargetFg( bool foreground );
/** @brief Set the features extracted from TrackerFeatureSet
@param features The features extracted
*/
void setFeatures( const Mat& features );
/** @brief Get the label. Return true for target foreground, false for background
*/
bool isTargetFg() const;
/** @brief Get the features extracted
*/
Mat getFeatures() const;
private:
bool isTarget;
Mat targetFeatures;
};
/** @brief Constructor
@param nFeatures Number of features for each sample
*/
TrackerStateEstimatorMILBoosting( int nFeatures = 250 );
~TrackerStateEstimatorMILBoosting();
/** @brief Set the current confidenceMap
@param confidenceMap The current :cConfidenceMap
*/
void setCurrentConfidenceMap( ConfidenceMap& confidenceMap );
protected:
Ptr<TrackerTargetState> estimateImpl( const std::vector<ConfidenceMap>& confidenceMaps );
void updateImpl( std::vector<ConfidenceMap>& confidenceMaps );
private:
uint max_idx( const std::vector<float> &v );
void prepareData( const ConfidenceMap& confidenceMap, Mat& positive, Mat& negative );
ClfMilBoost boostMILModel;
bool trained;
int numFeatures;
ConfidenceMap currentConfidenceMap;
};
/** @brief TrackerStateEstimatorAdaBoosting based on ADA-Boosting
*/
class CV_EXPORTS TrackerStateEstimatorAdaBoosting : public TrackerStateEstimator
{
public:
/** @brief Implementation of the target state for TrackerAdaBoostingTargetState
*/
class TrackerAdaBoostingTargetState : public TrackerTargetState
{
public:
/**
* \brief Constructor
* \param position Top left corner of the bounding box
* \param width Width of the bounding box
* \param height Height of the bounding box
* \param foreground label for target or background
* \param responses list of features
*/
TrackerAdaBoostingTargetState( const Point2f& position, int width, int height, bool foreground, const Mat& responses );
/**
* \brief Destructor
*/
~TrackerAdaBoostingTargetState()
{
}
;
/** @brief Set the features extracted from TrackerFeatureSet
@param responses The features extracted
*/
void setTargetResponses( const Mat& responses );
/** @brief Set label: true for target foreground, false for background
@param foreground Label for background/foreground
*/
void setTargetFg( bool foreground );
/** @brief Get the features extracted
*/
Mat getTargetResponses() const;
/** @brief Get the label. Return true for target foreground, false for background
*/
bool isTargetFg() const;
private:
bool isTarget;
Mat targetResponses;
};
/** @brief Constructor
@param numClassifer Number of base classifiers
@param initIterations Number of iterations in the initialization
@param nFeatures Number of features/weak classifiers
@param patchSize tracking rect
@param ROI initial ROI
*/
TrackerStateEstimatorAdaBoosting( int numClassifer, int initIterations, int nFeatures, Size patchSize, const Rect& ROI );
/**
* \brief Destructor
*/
~TrackerStateEstimatorAdaBoosting();
/** @brief Get the sampling ROI
*/
Rect getSampleROI() const;
/** @brief Set the sampling ROI
@param ROI the sampling ROI
*/
void setSampleROI( const Rect& ROI );
/** @brief Set the current confidenceMap
@param confidenceMap The current :cConfidenceMap
*/
void setCurrentConfidenceMap( ConfidenceMap& confidenceMap );
/** @brief Get the list of the selected weak classifiers for the classification step
*/
std::vector<int> computeSelectedWeakClassifier();
/** @brief Get the list of the weak classifiers that should be replaced
*/
std::vector<int> computeReplacedClassifier();
/** @brief Get the list of the weak classifiers that replace those to be replaced
*/
std::vector<int> computeSwappedClassifier();
protected:
Ptr<TrackerTargetState> estimateImpl( const std::vector<ConfidenceMap>& confidenceMaps );
void updateImpl( std::vector<ConfidenceMap>& confidenceMaps );
Ptr<StrongClassifierDirectSelection> boostClassifier;
private:
int numBaseClassifier;
int iterationInit;
int numFeatures;
bool trained;
Size initPatchSize;
Rect sampleROI;
std::vector<int> replacedClassifier;
std::vector<int> swappedClassifier;
ConfidenceMap currentConfidenceMap;
};
/**
* \brief TrackerStateEstimator based on SVM
*/
class CV_EXPORTS TrackerStateEstimatorSVM : public TrackerStateEstimator
{
public:
TrackerStateEstimatorSVM();
~TrackerStateEstimatorSVM();
protected:
Ptr<TrackerTargetState> estimateImpl( const std::vector<ConfidenceMap>& confidenceMaps );
void updateImpl( std::vector<ConfidenceMap>& confidenceMaps );
};
/************************************ Specific TrackerSamplerAlgorithm Classes ************************************/
/** @brief TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL
*/
class CV_EXPORTS TrackerSamplerCSC : public TrackerSamplerAlgorithm
{
public:
enum
{
MODE_INIT_POS = 1, //!< mode for init positive samples
MODE_INIT_NEG = 2, //!< mode for init negative samples
MODE_TRACK_POS = 3, //!< mode for update positive samples
MODE_TRACK_NEG = 4, //!< mode for update negative samples
MODE_DETECT = 5 //!< mode for detect samples
};
struct CV_EXPORTS Params
{
Params();
float initInRad; //!< radius for gathering positive instances during init
float trackInPosRad; //!< radius for gathering positive instances during tracking
float searchWinSize; //!< size of search window
int initMaxNegNum; //!< # negative samples to use during init
int trackMaxPosNum; //!< # positive samples to use during training
int trackMaxNegNum; //!< # negative samples to use during training
};
/** @brief Constructor
@param parameters TrackerSamplerCSC parameters TrackerSamplerCSC::Params
*/
TrackerSamplerCSC( const TrackerSamplerCSC::Params ¶meters = TrackerSamplerCSC::Params() );
/** @brief Set the sampling mode of TrackerSamplerCSC
@param samplingMode The sampling mode
The modes are:
- "MODE_INIT_POS = 1" -- for the positive sampling in initialization step
- "MODE_INIT_NEG = 2" -- for the negative sampling in initialization step
- "MODE_TRACK_POS = 3" -- for the positive sampling in update step
- "MODE_TRACK_NEG = 4" -- for the negative sampling in update step
- "MODE_DETECT = 5" -- for the sampling in detection step
*/
void setMode( int samplingMode );
~TrackerSamplerCSC();
protected:
bool samplingImpl( const Mat& image, Rect boundingBox, std::vector<Mat>& sample );
private:
Params params;
int mode;
RNG rng;
std::vector<Mat> sampleImage( const Mat& img, int x, int y, int w, int h, float inrad, float outrad = 0, int maxnum = 1000000 );
};
/** @brief TrackerSampler based on CS (current state), used by algorithm TrackerBoosting
*/
class CV_EXPORTS TrackerSamplerCS : public TrackerSamplerAlgorithm
{
public:
enum
{
MODE_POSITIVE = 1, //!< mode for positive samples
MODE_NEGATIVE = 2, //!< mode for negative samples
MODE_CLASSIFY = 3 //!< mode for classify samples
};
struct CV_EXPORTS Params
{
Params();
float overlap; //!<overlapping for the search windows
float searchFactor; //!<search region parameter
};
/** @brief Constructor
@param parameters TrackerSamplerCS parameters TrackerSamplerCS::Params
*/
TrackerSamplerCS( const TrackerSamplerCS::Params ¶meters = TrackerSamplerCS::Params() );
/** @brief Set the sampling mode of TrackerSamplerCS
@param samplingMode The sampling mode
The modes are:
- "MODE_POSITIVE = 1" -- for the positive sampling
- "MODE_NEGATIVE = 2" -- for the negative sampling
- "MODE_CLASSIFY = 3" -- for the sampling in classification step
*/
void setMode( int samplingMode );
~TrackerSamplerCS();
bool samplingImpl( const Mat& image, Rect boundingBox, std::vector<Mat>& sample );
Rect getROI() const;
private:
Rect getTrackingROI( float searchFactor );
Rect RectMultiply( const Rect & rect, float f );
std::vector<Mat> patchesRegularScan( const Mat& image, Rect trackingROI, Size patchSize );
void setCheckedROI( Rect imageROI );
Params params;
int mode;
Rect trackedPatch;
Rect validROI;
Rect ROI;
};
/** @brief 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.
*/
class CV_EXPORTS TrackerSamplerPF : public TrackerSamplerAlgorithm
{
public:
/** @brief 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.
*/
struct CV_EXPORTS Params
{
Params();
int iterationNum; //!< number of selection rounds
int particlesNum; //!< number of "perturbed" boxes on each round
double alpha; //!< with each new round we exponentially decrease the amount of "perturbing" we allow (like in simulated annealing)
//!< and this very alpha controls how fast annealing happens, ie. how fast perturbing decreases
Mat_<double> std; //!< initial values for perturbing (1-by-4 array, as each rectangle is given by 4 values -- coordinates of opposite vertices,
//!< hence we have 4 values to perturb)
};
/** @brief Constructor
@param chosenRect Initial rectangle, that is supposed to contain target we'd like to track.
@param parameters
*/
TrackerSamplerPF(const Mat& chosenRect,const TrackerSamplerPF::Params ¶meters = TrackerSamplerPF::Params());
protected:
bool samplingImpl( const Mat& image, Rect boundingBox, std::vector<Mat>& sample );
private:
Params params;
Ptr<MinProblemSolver> _solver;
Ptr<MinProblemSolver::Function> _function;
};
/************************************ Specific TrackerFeature Classes ************************************/
/**
* \brief TrackerFeature based on Feature2D
*/
class CV_EXPORTS TrackerFeatureFeature2d : public TrackerFeature
{
public:
/**
* \brief Constructor
* \param detectorType string of FeatureDetector
* \param descriptorType string of DescriptorExtractor
*/
TrackerFeatureFeature2d( String detectorType, String descriptorType );
~TrackerFeatureFeature2d();
void selection( Mat& response, int npoints );
protected:
bool computeImpl( const std::vector<Mat>& images, Mat& response );
private:
std::vector<KeyPoint> keypoints;
};
/**
* \brief TrackerFeature based on HOG
*/
class CV_EXPORTS TrackerFeatureHOG : public TrackerFeature
{
public:
TrackerFeatureHOG();
~TrackerFeatureHOG();
void selection( Mat& response, int npoints );
protected:
bool computeImpl( const std::vector<Mat>& images, Mat& response );
};
/** @brief TrackerFeature based on HAAR features, used by TrackerMIL and many others algorithms
@note HAAR features implementation is copied from apps/traincascade and modified according to MIL
*/
class CV_EXPORTS TrackerFeatureHAAR : public TrackerFeature
{
public:
struct CV_EXPORTS Params
{
Params();
int numFeatures; //!< # of rects
Size rectSize; //!< rect size
bool isIntegral; //!< true if input images are integral, false otherwise
};
/** @brief Constructor
@param parameters TrackerFeatureHAAR parameters TrackerFeatureHAAR::Params
*/
TrackerFeatureHAAR( const TrackerFeatureHAAR::Params ¶meters = TrackerFeatureHAAR::Params() );
~TrackerFeatureHAAR();
/** @brief Compute the features only for the selected indices in the images collection
@param selFeatures indices of selected features
@param images The images
@param response Collection of response for the specific TrackerFeature
*/
bool extractSelected( const std::vector<int> selFeatures, const std::vector<Mat>& images, Mat& response );