Common Interfaces of TrackerModel¶

ConfidenceMap¶

Represents the model of the target at frame $k$ (all states and scores)

[AAM] The set of the pair $\langle \hat{x}^{i}_{k}, C^{i}_{k} \rangle$

ConfidenceMap¶

ConfidenceMap:

typedef std::vector<std::pair<Ptr<TrackerTargetState>, float> > ConfidenceMap;

Trajectory¶

Represents the estimate states for all frames

[AAM] $x_{k}$ is the trajectory of the target up to time $k$

Trajectory¶

Trajectory:

typedef std::vector<Ptr<TrackerTargetState> > Trajectory;

TrackerTargetState¶

Abstract base class for TrackerTargetState that represents a possible state of the target.

[AAM] $\hat{x}^{i}_{k}$ all the states candidates.

Inherits this class with your Target state

class TrackerTargetState¶

TrackerTargetState class:

class CV_EXPORTS_W TrackerTargetState
{
 public:
  virtual ~TrackerTargetState(){};

  Point2f getTargetPosition() const;
  void setTargetPosition( const Point2f& position );

  int getTargetWidth() const;
  void setTargetWidth( int width );

  int getTargetHeight() const;
  void setTargetHeight( int height );

};

In own implementation you can add scale variation, width, height, orientation, etc.

TrackerStateEstimator¶

Abstract base class for TrackerStateEstimator that estimates the most likely target state.

[AAM] State estimator

[AMVOT] Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid)

class TrackerStateEstimator¶

TrackerStateEstimator class:

class CV_EXPORTS_W TrackerStateEstimator
{
 public:
  virtual ~TrackerStateEstimator();

  static Ptr<TrackerStateEstimator> create( const String& trackeStateEstimatorType );

  Ptr<TrackerTargetState> estimate( const std::vector<ConfidenceMap>& confidenceMaps );
  void update( std::vector<ConfidenceMap>& confidenceMaps );

  String getClassName() const;

};

TrackerStateEstimator::create¶

Create TrackerStateEstimator by tracker state estimator type

C++: static Ptr<TrackerStateEstimator> TrackerStateEstimator::create(const String& trackeStateEstimatorType)¶

Parameters:	trackeStateEstimatorType – The TrackerStateEstimator name

The modes available now:

"BOOSTING" – Boosting-based discriminative appearance models. See [AMVOT] section 4.4

The modes available soon:

"SVM" – SVM-based discriminative appearance models. See [AMVOT] section 4.5

TrackerStateEstimator::estimate¶

Estimate the most likely target state, return the estimated state

C++: Ptr<TrackerTargetState> TrackerStateEstimator::estimate(const std::vector<ConfidenceMap>& confidenceMaps)¶

Parameters:	confidenceMaps – The overall appearance model as a list of `ConfidenceMap`

TrackerStateEstimator::update¶

Update the ConfidenceMap with the scores

C++: void TrackerStateEstimator::update(std::vector<ConfidenceMap>& confidenceMaps)¶

Parameters:	confidenceMaps – The overall appearance model as a list of `ConfidenceMap`

TrackerStateEstimator::getClassName¶

Get the name of the specific TrackerStateEstimator

C++: String TrackerStateEstimator::getClassName() const¶

TrackerModel¶

Abstract class that represents the model of the target. It must be instantiated by specialized tracker

[AAM] Ak

Inherits this with your TrackerModel

class TrackerModel¶

TrackerModel class:

class CV_EXPORTS_W TrackerModel
{
 public:

  TrackerModel();
  virtual ~TrackerModel();

  void modelEstimation( const std::vector<Mat>& responses );
  void modelUpdate();
  bool runStateEstimator();

  bool setTrackerStateEstimator( Ptr<TrackerStateEstimator> trackerStateEstimator );
  void setLastTargetState( const Ptr<TrackerTargetState>& lastTargetState );

  Ptr<TrackerTargetState> getLastTargetState() const;
  const std::vector<ConfidenceMap>& getConfidenceMaps() const;
  const ConfidenceMap& getLastConfidenceMap() const;
  Ptr<TrackerStateEstimator> getTrackerStateEstimator() const;
};

TrackerModel::modelEstimation¶

Estimate the most likely target location

[AAM] ME, Model Estimation table I

C++: void TrackerModel::modelEstimation(const std::vector<Mat>& responses)¶

Parameters:	responses – Features extracted from `TrackerFeatureSet`

TrackerModel::modelUpdate¶

Update the model

[AAM] MU, Model Update table I

C++: void TrackerModel::modelUpdate()¶

TrackerModel::runStateEstimator¶

Run the TrackerStateEstimator, return true if is possible to estimate a new state, false otherwise

C++: bool TrackerModel::runStateEstimator()¶

TrackerModel::setTrackerStateEstimator¶

Set TrackerEstimator, return true if the tracker state estimator is added, false otherwise

C++: bool TrackerModel::setTrackerStateEstimator(Ptr<TrackerStateEstimator> trackerStateEstimator)¶

Parameters:	trackerStateEstimator – The `TrackerStateEstimator`

Note

You can add only one TrackerStateEstimator

TrackerModel::setLastTargetState¶

Set the current TrackerTargetState in the Trajectory

C++: void TrackerModel::setLastTargetState(const Ptr<TrackerTargetState>& lastTargetState)¶

Parameters:	lastTargetState – The current `TrackerTargetState`

TrackerModel::getLastTargetState¶

Get the last TrackerTargetState from Trajectory

C++: Ptr<TrackerTargetState> TrackerModel::getLastTargetState() const¶

TrackerModel::getConfidenceMaps¶

Get the list of the ConfidenceMap

C++: const std::vector<ConfidenceMap>& TrackerModel::getConfidenceMaps() const¶

TrackerModel::getLastConfidenceMap¶

Get the last ConfidenceMap for the current frame

C++: const ConfidenceMap& TrackerModel::getLastConfidenceMap() const¶

TrackerModel::getTrackerStateEstimator¶

Get the TrackerStateEstimator

C++: Ptr<TrackerStateEstimator> TrackerModel::getTrackerStateEstimator() const¶

Specialized TrackerStateEstimator¶

In [AMVOT] Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid) are described the most known statistical model.

At moment TrackerStateEstimatorMILBoosting and TrackerStateEstimatorAdaBoosting are implemented.

TrackerStateEstimatorMILBoosting : TrackerStateEstimator¶

TrackerStateEstimator based on Boosting

class TrackerStateEstimatorMILBoosting¶

TrackerStateEstimatorMILBoosting class:

class CV_EXPORTS_W TrackerStateEstimatorMILBoosting : public TrackerStateEstimator
{
 public:
  class TrackerMILTargetState : public TrackerTargetState
  {
   ...
  };
  TrackerStateEstimatorMILBoosting( int nFeatures = 250 );
  ~TrackerStateEstimatorMILBoosting();

  void setCurrentConfidenceMap( ConfidenceMap& confidenceMap );
};

TrackerMILTargetState : TrackerTargetState¶

Implementation of the target state for TrackerMILTargetState

class TrackerMILTargetState¶

TrackerMILTargetState class:

class TrackerMILTargetState : public TrackerTargetState
{
 public:
 TrackerMILTargetState( const Point2f& position, int targetWidth, int targetHeight, bool foreground, const Mat& features );
 ~TrackerMILTargetState(){};

 void setTargetFg( bool foreground );
 void setFeatures( const Mat& features );
 bool isTargetFg() const;
 Mat getFeatures() const;
};

TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setTargetFg¶

Set label: true for target foreground, false for background

C++: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setTargetFg(bool foreground)¶

Parameters:	foreground – Label for background/foreground

TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures¶

Set the features extracted from TrackerFeatureSet

C++: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures(const Mat& features)¶

Parameters:	features – The features extracted

TrackerStateEstimatorMILBoosting::TrackerMILTargetState::isTargetFg¶

Get the label. Return true for target foreground, false for background

C++: bool TrackerStateEstimatorMILBoosting::TrackerMILTargetState::isTargetFg() const¶

TrackerStateEstimatorMILBoosting::TrackerMILTargetState::getFeatures¶

Get the features extracted

C++: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures(const Mat& features)

TrackerStateEstimatorMILBoosting::TrackerStateEstimatorMILBoosting¶

Constructor

C++: TrackerStateEstimatorMILBoosting::TrackerStateEstimatorMILBoosting(int nFeatures=250 )¶

Parameters:	nFeatures – Number of features for each sample

TrackerStateEstimatorMILBoosting::setCurrentConfidenceMap¶

Set the current confidenceMap

C++: void TrackerStateEstimatorMILBoosting::setCurrentConfidenceMap(ConfidenceMap& confidenceMap)¶

Parameters:	confidenceMap – The current `ConfidenceMap`

TrackerStateEstimatorAdaBoosting : TrackerStateEstimator¶

TrackerStateEstimatorAdaBoosting based on ADA-Boosting

class TrackerStateEstimatorAdaBoosting¶

TrackerStateEstimatorAdaBoosting class:

class CV_EXPORTS_W TrackerStateEstimatorAdaBoosting : public TrackerStateEstimator
{
 public:
  class TrackerAdaBoostingTargetState : public TrackerTargetState
  {
   ...
  };
  TrackerStateEstimatorAdaBoosting( int numClassifer, int initIterations, int nFeatures, Size patchSize, const Rect& ROI, const std::vector<std::pair<float, float> >& meanSigma );
  ~TrackerStateEstimatorAdaBoosting();

  Rect getSampleROI() const;
  void setSampleROI( const Rect& ROI );
  void setCurrentConfidenceMap( ConfidenceMap& confidenceMap );
  std::vector<int> computeSelectedWeakClassifier();
  std::vector<int> computeReplacedClassifier();
  std::vector<int> computeSwappedClassifier();
  void setMeanSigmaPair( const std::vector<std::pair<float, float> >& meanSigmaPair );
};

TrackerAdaBoostingTargetState : TrackerTargetState¶

Implementation of the target state for TrackerAdaBoostingTargetState

class TrackerAdaBoostingTargetState¶

TrackerAdaBoostingTargetState class:

class TrackerAdaBoostingTargetState : public TrackerTargetState
{
 public:
 TrackerAdaBoostingTargetState( const Point2f& position, int width, int height, bool foreground, const Mat& responses );
 ~TrackerAdaBoostingTargetState(){};

 void setTargetResponses( const Mat& responses );
 void setTargetFg( bool foreground );
 Mat getTargetResponses() const;
 bool isTargetFg() const;
};

TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetFg¶

Set label: true for target foreground, false for background

C++: void TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetFg(bool foreground)¶

Parameters:	foreground – Label for background/foreground

TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetResponses¶

Set the features extracted from TrackerFeatureSet

C++: void TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetResponses(const Mat& responses)¶

Parameters:	responses – The features extracted

TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::isTargetFg¶

Get the label. Return true for target foreground, false for background

C++: bool TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::isTargetFg() const¶

TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::getTargetResponses¶

Get the features extracted

C++: Mat TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::getTargetResponses()¶

TrackerStateEstimatorAdaBoosting::TrackerStateEstimatorAdaBoosting¶

Constructor

C++: TrackerStateEstimatorAdaBoosting::TrackerStateEstimatorAdaBoosting(int numClassifer, int initIterations, int nFeatures, Size patchSize, const Rect& ROI, const std::vector<std::pair<float, float>>& meanSigma)¶

Parameters:	numClassifer – Number of base classifiers initIterations – Number of iterations in the initialization nFeatures – Number of features/weak classifiers patchSize – tracking rect ROI – initial ROI meanSigma – pairs of mean/sigma

TrackerStateEstimatorAdaBoosting::setCurrentConfidenceMap¶

Set the current confidenceMap

C++: void TrackerStateEstimatorAdaBoosting::setCurrentConfidenceMap(ConfidenceMap& confidenceMap)¶

Parameters:	confidenceMap – The current `ConfidenceMap`

TrackerStateEstimatorAdaBoosting::getSampleROI¶

Get the sampling ROI

C++: Rect TrackerStateEstimatorAdaBoosting::getSampleROI() const¶

TrackerStateEstimatorAdaBoosting::setSampleROI¶

Set the sampling ROI

C++: void TrackerStateEstimatorAdaBoosting::setSampleROI(const Rect& ROI)¶

Parameters:	ROI – the sampling ROI

TrackerStateEstimatorAdaBoosting::computeSelectedWeakClassifier¶

Get the list of the selected weak classifiers for the classification step

C++: std::vector<int> TrackerStateEstimatorAdaBoosting::computeSelectedWeakClassifier()¶

TrackerStateEstimatorAdaBoosting::computeReplacedClassifier¶

Get the list of the weak classifiers that should be replaced

C++: std::vector<int> TrackerStateEstimatorAdaBoosting::computeReplacedClassifier()¶

TrackerStateEstimatorAdaBoosting::computeSwappedClassifier¶

Get the list of the weak classifiers that replace those to be replaced

C++: std::vector<int> TrackerStateEstimatorAdaBoosting::computeSwappedClassifier()¶

TrackerStateEstimatorAdaBoosting::setMeanSigmaPair¶

Set the mean/sigma to instantiate possibly new classifiers

C++: void TrackerStateEstimatorAdaBoosting::setMeanSigmaPair(const std::vector<std::pair<float, float>>& meanSigmaPair)¶

Parameters:	meanSigmaPair – the mean/sigma pairs

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Common Interfaces of TrackerModel¶

ConfidenceMap¶

Trajectory¶

TrackerTargetState¶

TrackerStateEstimator¶

TrackerStateEstimator::create¶

TrackerStateEstimator::estimate¶

TrackerStateEstimator::update¶

TrackerStateEstimator::getClassName¶

TrackerModel¶

TrackerModel::modelEstimation¶

TrackerModel::modelUpdate¶

TrackerModel::runStateEstimator¶

TrackerModel::setTrackerStateEstimator¶

TrackerModel::setLastTargetState¶

TrackerModel::getLastTargetState¶

TrackerModel::getConfidenceMaps¶

TrackerModel::getLastConfidenceMap¶

TrackerModel::getTrackerStateEstimator¶

Specialized TrackerStateEstimator¶

TrackerStateEstimatorMILBoosting : TrackerStateEstimator¶

TrackerMILTargetState : TrackerTargetState¶

TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setTargetFg¶

TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures¶

TrackerStateEstimatorMILBoosting::TrackerMILTargetState::isTargetFg¶

TrackerStateEstimatorMILBoosting::TrackerMILTargetState::getFeatures¶

TrackerStateEstimatorMILBoosting::TrackerStateEstimatorMILBoosting¶

TrackerStateEstimatorMILBoosting::setCurrentConfidenceMap¶

TrackerStateEstimatorAdaBoosting : TrackerStateEstimator¶

TrackerAdaBoostingTargetState : TrackerTargetState¶

TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetFg¶

TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetResponses¶

TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::isTargetFg¶

TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::getTargetResponses¶

TrackerStateEstimatorAdaBoosting::TrackerStateEstimatorAdaBoosting¶

TrackerStateEstimatorAdaBoosting::setCurrentConfidenceMap¶

TrackerStateEstimatorAdaBoosting::getSampleROI¶

TrackerStateEstimatorAdaBoosting::setSampleROI¶

TrackerStateEstimatorAdaBoosting::computeSelectedWeakClassifier¶

TrackerStateEstimatorAdaBoosting::computeReplacedClassifier¶

TrackerStateEstimatorAdaBoosting::computeSwappedClassifier¶

TrackerStateEstimatorAdaBoosting::setMeanSigmaPair¶

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