Common Interfaces of TrackerSampler

TrackerSampler

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

class TrackerSampler

TrackerSampler class:

class CV_EXPORTS_W TrackerSampler
{
 public:

  TrackerSampler();
  ~TrackerSampler();

  void sampling( const Mat& image, Rect boundingBox );

  const std::vector<std::pair<String, Ptr<TrackerSamplerAlgorithm> > >& getSamplers() const;
  const std::vector<Mat>& getSamples() const;

  bool addTrackerSamplerAlgorithm( String trackerSamplerAlgorithmType );
  bool addTrackerSamplerAlgorithm( Ptr<TrackerSamplerAlgorithm>& sampler );

};

TrackerSampler is an aggregation of TrackerSamplerAlgorithm

See also

TrackerSamplerAlgorithm

TrackerSampler::sampling

Computes the regions starting from a position in an image

C++: void TrackerSampler::sampling(const Mat& image, Rect boundingBox)

Parameters:

• image – The current frame
• boundingBox – The bounding box from which regions can be calculated

TrackerSampler::getSamplers

Return the collection of the TrackerSamplerAlgorithm

C++: const std::vector<std::pair<String, Ptr<TrackerSamplerAlgorithm>>>& TrackerSampler::getSamplers() const

TrackerSampler::getSamples

Return the samples from all TrackerSamplerAlgorithm, [AAM] Fig. 1 variable Sk

C++: const std::vector<Mat>& TrackerSampler::getSamples() const

TrackerSampler::addTrackerSamplerAlgorithm

Add TrackerSamplerAlgorithm in the collection. Return true if sampler is added, false otherwise

C++: bool TrackerSampler::addTrackerSamplerAlgorithm(String trackerSamplerAlgorithmType)

Parameters:

• trackerSamplerAlgorithmType – The TrackerSamplerAlgorithm name

C++: bool TrackerSampler::addTrackerSamplerAlgorithm(Ptr<TrackerSamplerAlgorithm>& sampler)

Parameters:

• sampler – The TrackerSamplerAlgorithm class

The modes available now:

• "CSC" – Current State Center
• "CS" – Current State
• "PF" – Particle Filtering

Example TrackerSamplerAlgorithm::addTrackerSamplerAlgorithm :

//sample usage:

 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" );

Note

If you use the second method, you must initialize the TrackerSamplerAlgorithm

TrackerSamplerAlgorithm

Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler.

class TrackerSamplerAlgorithm

TrackerSamplerAlgorithm class:

class CV_EXPORTS_W TrackerSamplerAlgorithm
{
 public:

  virtual ~TrackerSamplerAlgorithm();

  static Ptr<TrackerSamplerAlgorithm> create( const String& trackerSamplerType );

  bool sampling( const Mat& image, Rect boundingBox, std::vector<Mat>& sample );
  String getClassName() const;
};

TrackerSamplerAlgorithm::create

Create TrackerSamplerAlgorithm by tracker sampler type.

C++: static Ptr<TrackerSamplerAlgorithm> TrackerSamplerAlgorithm::create(const String& trackerSamplerType)

Parameters:

• trackerSamplerType – The trackerSamplerType name

The modes available now:

• "CSC" – Current State Center
• "CS" – Current State

TrackerSamplerAlgorithm::sampling

Computes the regions starting from a position in an image. Return true if samples are computed, false otherwise

C++: bool TrackerSamplerAlgorithm::sampling(const Mat& image, Rect boundingBox, std::vector<Mat>& sample)

Parameters:

• image – The current frame
• boundingBox – The bounding box from which regions can be calculated

Sample:

The computed samples [AAM] Fig. 1 variable Sk

TrackerSamplerAlgorithm::getClassName

Get the name of the specific TrackerSamplerAlgorithm

C++: String TrackerSamplerAlgorithm::getClassName() const

Specialized TrackerSamplerAlgorithm

In [AAM] table I there are described the most known sampling strategies. At moment TrackerSamplerCSC and TrackerSamplerCS are implemented. Beside these, there is TrackerSamplerPF, sampler based on particle filtering.

TrackerSamplerCSC : TrackerSamplerAlgorithm

TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL

class TrackerSamplerCSC

TrackerSamplerCSC class:

class CV_EXPORTS_W TrackerSamplerCSC
{
 public:

  TrackerSamplerCSC( const TrackerSamplerCSC::Params &parameters = TrackerSamplerCSC::Params() );
  void setMode( int samplingMode );

  ~TrackerSamplerCSC();
};

TrackerSamplerCSC::Params

struct TrackerSamplerCSC::Params

List of TrackerSamplerCSC parameters:

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
};

TrackerSamplerCSC::TrackerSamplerCSC

Constructor

C++: TrackerSamplerCSC::TrackerSamplerCSC(const TrackerSamplerCSC::Params& parameters=TrackerSamplerCSC::Params() )

Parameters:

• parameters – TrackerSamplerCSC parameters TrackerSamplerCSC::Params

TrackerSamplerCSC::setMode

Set the sampling mode of TrackerSamplerCSC

C++: void TrackerSamplerCSC::setMode(int samplingMode)

Parameters:

• 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

TrackerSamplerCS : TrackerSamplerAlgorithm

TrackerSampler based on CS (current state), used by algorithm TrackerBoosting

class TrackerSamplerCS

TrackerSamplerCS class:

class CV_EXPORTS_W TrackerSamplerCS
{
 public:

  TrackerSamplerCS( const TrackerSamplerCS::Params &parameters = TrackerSamplerCS::Params() );
  void setMode( int samplingMode );

  ~TrackerSamplerCS();
};

TrackerSamplerCS::Params

struct TrackerSamplerCS::Params

List of TrackerSamplerCS parameters:

struct CV_EXPORTS Params
{
 Params();
 float overlap;  //overlapping for the search windows
 float searchFactor; //search region parameter
};

TrackerSamplerCS::TrackerSamplerCS

Constructor

C++: TrackerSamplerCS::TrackerSamplerCS(const TrackerSamplerCS::Params& parameters=TrackerSamplerCS::Params() )

Parameters:

• parameters – TrackerSamplerCS parameters TrackerSamplerCS::Params

TrackerSamplerCS::setMode

Set the sampling mode of TrackerSamplerCS

C++: void TrackerSamplerCS::setMode(int samplingMode)

Parameters:

• 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

TrackerSamplerPF : TrackerSamplerAlgorithm

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 TrackerSamplerPF

TrackerSamplerPF class:

class CV_EXPORTS_W TrackerSamplerPF : public TrackerSamplerAlgorithm{
public:
  TrackerSamplerPF(const Mat& chosenRect,const TrackerSamplerPF::Params &parameters = TrackerSamplerPF::Params());
  void sampling( const Mat& image, Rect boundingBox, std::vector<Mat>& sample ); //inherited from TrackerSamplerAlgorithmTrackerSamplerAlgorithm
};

TrackerSamplerPF::Params

struct TrackerSamplerPF::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.

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)
};

TrackerSamplerPF::TrackerSamplerPF

Constructor

C++: TrackerSamplerPF(const Mat& chosenRect, const TrackerSamplerPF::Params& parameters=TrackerSamplerPF::Params())

Parameters:

• chosenRect – Initial rectangle, that is supposed to contain target we’d like to track.