2014新跟踪算法KCF笔记 --续（代码部分）

KCF跟踪在opencv３.1中集成了，在opencv_contrib/tracking中有，opencv_contrib这个需要重新编译一下opencv３.1才能ｇｅｔ．ｗｉｎｄｏｗｓ下的编译方法如下网址
http://blog.csdn.net/yomo127/article/details/50474955
可以在git上直接下载也可，地址如下
https://github.com/Itseez/opencv_contrib
如果你是直接从ｇｉｔ下载的，可以看到关于ＫＣＦ部分添加的内容如下（ｇｉｔ　ｌｏｇ可）

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/*---------------STEP 1---------------------*/

/* modify this file

* opencv2/tracking/tracker.hpp

* and put several lines of snippet similar to

* the following:

*/

/*------------------------------------------*/

class CV_EXPORTS_W TrackerKCF : public Tracker

{

public:

struct CV_EXPORTS Params

{

Params();

void read( const FileNode& /*fn*/ );

void write( FileStorage& /*fs*/ ) const;

};

/** @brief Constructor

@param parameters KCF parameters TrackerKCF::Params

*/

BOILERPLATE_CODE("KCF",TrackerKCF);

};

/*---------------STEP 2---------------------*/

/* modify this file

* src/tracker.cpp

* add one line in function

* Ptr<Tracker> Tracker::create( const String& trackerType )

*/

/*------------------------------------------*/

Ptr<Tracker> Tracker::create( const String& trackerType )

{

BOILERPLATE_CODE("MIL",TrackerMIL);

BOILERPLATE_CODE("BOOSTING",TrackerBoosting);

BOILERPLATE_CODE("MEDIANFLOW",TrackerMedianFlow);

BOILERPLATE_CODE("TLD",TrackerTLD);

BOILERPLATE_CODE("KCF",TrackerKCF); // add this line!

return Ptr<Tracker>();

}

/*---------------STEP 3---------------------*/

/* make a new file and paste the snippet below

* and modify it according to your needs.

* also make sure to put the LICENSE part.

* src/trackerKCF.cpp

*/

/*------------------------------------------*/

/*---------------------------

| TrackerKCFModel

|---------------------------*/

namespace cv{

/**

* \brief Implementation of TrackerModel for MIL algorithm

*/

class TrackerKCFModel : public TrackerModel{

public:

TrackerKCFModel(TrackerKCF::Params /*params*/){}

~TrackerKCFModel(){}

protected:

void modelEstimationImpl( const std::vector<Mat>& responses ){}

void modelUpdateImpl(){}

};

} /* namespace cv */

/*---------------------------

| TrackerKCF

|---------------------------*/

namespace cv{

/*

* Prototype

*/

class TrackerKCFImpl : public TrackerKCF{

public:

TrackerKCFImpl( const TrackerKCF::Params ¶meters = TrackerKCF::Params() );

void read( const FileNode& fn );

void write( FileStorage& fs ) const;

protected:

bool initImpl( const Mat& image, const Rect2d& boundingBox );

bool updateImpl( const Mat& image, Rect2d& boundingBox );

TrackerKCF::Params params;

};

/*

* Constructor

*/

Ptr<TrackerKCF> TrackerKCF::createTracker(const TrackerKCF::Params ¶meters){

return Ptr<TrackerKCFImpl>(new TrackerKCFImpl(parameters));

}

TrackerKCFImpl::TrackerKCFImpl( const TrackerKCF::Params ¶meters ) :

params( parameters )

{

isInit = false;

}

void TrackerKCFImpl::read( const cv::FileNode& fn ){

params.read( fn );

}

void TrackerKCFImpl::write( cv::FileStorage& fs ) const{

params.write( fs );

}

bool TrackerKCFImpl::initImpl( const Mat& image, const Rect2d& boundingBox ){

model=Ptr<TrackerKCFModel>(new TrackerKCFModel(params));

return true;

}

bool TrackerKCFImpl::updateImpl( const Mat& image, Rect2d& boundingBox ){return true;}

/*

* Parameters

*/

TrackerKCF::Params::Params(){

}

void TrackerKCF::Params::read( const cv::FileNode& fn ){

}

void TrackerKCF::Params::write( cv::FileStorage& fs ) const{

}

} /* namespace cv */

然后再来看看/modules/tracking/include/opencv2/tracking/tracker.hpp中关于kcf的定义，多说两句，这里面定义了很多跟踪的方法，包括在线跟踪的MILtracker,OBAtracker，还有,particle filtering tracker,TLD tracker,KCF tracker，他们的基类叫做Tracker,这个Tracker的声明如下：

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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( const Mat& 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( const Mat& image, CV_OUT Rect2d& boundingBox );

/** @brief Creates a tracker by its name.

@param trackerType Tracker type

The following detector types are supported:

- "MIL" -- TrackerMIL

- "BOOSTING" -- TrackerBoosting

*/

CV_WRAP static Ptr<Tracker> create( const String& trackerType );

virtual void read( const FileNode& fn )=0;

virtual void write( FileStorage& fs ) const=0;

Ptr<TrackerModel> getModel()

{

return model;

}

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;

};

下面看一下继承了Tracker这个KCF　ｔｒａｃｋｅｒ，这个算法的论文链接在上一篇博客里

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/** @brief KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed.

* This tracking method is an implementation of @cite KCF_ECCV which is extended to KFC with color-names features (@cite KCF_CN).

* The original paper of KCF is available at <http://home.isr.uc.pt/~henriques/circulant/index.html>

* as well as the matlab implementation. For more information about KCF with color-names features, please refer to

* <http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html>.

*/

class CV_EXPORTS TrackerKCF : public Tracker

{

public:

/**

* \brief Feature type to be used in the tracking grayscale, colornames, compressed color-names

* The modes available now:

- "GRAY" -- Use grayscale values as the feature

- "CN" -- Color-names feature

*/

enum MODE {

GRAY = (1u << 0),

CN = (1u << 1),

CUSTOM = (1u << 2)

};

struct CV_EXPORTS Params

{

/**

* \brief Constructor

*/

Params();

/**

* \brief Read parameters from file, currently unused

*/

void read(const FileNode& /*fn*/);

/**

* \brief Read parameters from file, currently unused

*/

void write(FileStorage& /*fs*/) const;

double sigma; //!< gaussian kernel bandwidth

double lambda; //!< regularization

double interp_factor; //!< linear interpolation factor for adaptation

double output_sigma_factor; //!< spatial bandwidth (proportional to target)

double pca_learning_rate; //!< compression learning rate

bool resize; //!< activate the resize feature to improve the processing speed

bool split_coeff; //!< split the training coefficients into two matrices

bool wrap_kernel; //!< wrap around the kernel values

bool compress_feature; //!< activate the pca method to compress the features

int max_patch_size; //!< threshold for the ROI size

int compressed_size; //!< feature size after compression

unsigned int desc_pca; //!< compressed descriptors of TrackerKCF::MODE

unsigned int desc_npca; //!< non-compressed descriptors of TrackerKCF::MODE

};

virtual void setFeatureExtractor(void(*)(const Mat, const Rect, Mat&), bool pca_func = false);

/** @brief Constructor

@param parameters KCF parameters TrackerKCF::Params

*/

BOILERPLATE_CODE("KCF", TrackerKCF);

};

好，上面这些只是介绍了在opencv中　KCF的接口，下面举一个例子来看看怎么调用

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//安装配置好opencv3.1

#include <opencv2/core/utility.hpp>

#include <opencv2/tracking.hpp>

#include <opencv2/videoio.hpp>

#include <opencv2/highgui.hpp>

#include <iostream>

#include <cstring>

using namespace std;

using namespace cv;

int main( ){

// declares all required variables

//! [vars]

Rect2d roi;

Mat frame;

//! [vars]

// create a tracker object

//! create这个函数的参数也可以是- "MIL" -- TrackerMIL

//! - "BOOSTING" -- TrackerBoosting ,还可以是　"TLD"，"MEDIANFLOW"

Ptr<Tracker> tracker = Tracker::create( "KCF" );

//! [create]

// set input video

//! [setvideo]

std::string video = "test.avi";

VideoCapture cap(video);

//! [setvideo]

// get bounding box

//! [getframe]

cap >> frame;

//! [getframe]

//! [selectroi]选择目标ｒｏｉ以ＧＵＩ的形式

roi=selectROI("tracker",frame);

//! [selectroi]

//quit if ROI was not selected

if(roi.width==0 || roi.height==0)

return 0;

// initialize the tracker

//! [init]

tracker->init(frame,roi);

//! [init]

// perform the tracking process

printf("Start the tracking process\n");

for ( ;; ){

// get frame from the video

cap >> frame;

// stop the program if no more images

if(frame.rows==0 || frame.cols==0)

break;

// update the tracking result

//! [update]

tracker->update(frame,roi);

//! [update]

//! [visualization]

// draw the tracked object

rectangle( frame, roi, Scalar( 255, 0, 0 ), 2, 1 );

// show image with the tracked object

imshow("tracker",frame);

//! [visualization]

//quit on ESC button

if(waitKey(1)==27)

　　break;

}

return 0;

}