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507 lines
16 KiB
ReStructuredText
507 lines
16 KiB
ReStructuredText
Common Interfaces of TrackerModel
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=================================
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.. highlight:: cpp
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ConfidenceMap
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-------------
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Represents the model of the target at frame :math:`k` (all states and scores)
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[AAM]_ The set of the pair :math:`\langle \hat{x}^{i}_{k}, C^{i}_{k} \rangle`
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.. c:type:: ConfidenceMap
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ConfidenceMap::
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typedef std::vector<std::pair<Ptr<TrackerTargetState>, float> > ConfidenceMap;
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.. seealso::
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:ocv:class:`TrackerTargetState`
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Trajectory
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----------
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Represents the estimate states for all frames
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[AAM]_ :math:`x_{k}` is the trajectory of the target up to time :math:`k`
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.. c:type:: Trajectory
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Trajectory::
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typedef std::vector<Ptr<TrackerTargetState> > Trajectory;
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.. seealso::
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:ocv:class:`TrackerTargetState`
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TrackerTargetState
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------------------
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Abstract base class for TrackerTargetState that represents a possible state of the target.
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[AAM]_ :math:`\hat{x}^{i}_{k}` all the states candidates.
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Inherits this class with your Target state
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.. ocv:class:: TrackerTargetState
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TrackerTargetState class::
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class CV_EXPORTS_W TrackerTargetState
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{
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public:
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virtual ~TrackerTargetState(){};
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Point2f getTargetPosition() const;
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void setTargetPosition( const Point2f& position );
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int getTargetWidth() const;
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void setTargetWidth( int width );
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int getTargetHeight() const;
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void setTargetHeight( int height );
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};
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In own implementation you can add scale variation, width, height, orientation, etc.
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TrackerStateEstimator
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---------------------
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Abstract base class for TrackerStateEstimator that estimates the most likely target state.
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[AAM]_ State estimator
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[AMVOT]_ Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid)
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.. ocv:class:: TrackerStateEstimator
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TrackerStateEstimator class::
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class CV_EXPORTS_W TrackerStateEstimator
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{
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public:
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virtual ~TrackerStateEstimator();
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static Ptr<TrackerStateEstimator> create( const String& trackeStateEstimatorType );
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Ptr<TrackerTargetState> estimate( const std::vector<ConfidenceMap>& confidenceMaps );
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void update( std::vector<ConfidenceMap>& confidenceMaps );
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String getClassName() const;
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};
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TrackerStateEstimator::create
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-----------------------------
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Create TrackerStateEstimator by tracker state estimator type
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.. ocv:function:: static Ptr<TrackerStateEstimator> TrackerStateEstimator::create( const String& trackeStateEstimatorType )
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:param trackeStateEstimatorType: The TrackerStateEstimator name
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The modes available now:
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* ``"BOOSTING"`` -- Boosting-based discriminative appearance models. See [AMVOT]_ section 4.4
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The modes available soon:
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* ``"SVM"`` -- SVM-based discriminative appearance models. See [AMVOT]_ section 4.5
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TrackerStateEstimator::estimate
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-------------------------------
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Estimate the most likely target state, return the estimated state
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.. ocv:function:: Ptr<TrackerTargetState> TrackerStateEstimator::estimate( const std::vector<ConfidenceMap>& confidenceMaps )
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:param confidenceMaps: The overall appearance model as a list of :c:type:`ConfidenceMap`
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TrackerStateEstimator::update
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-----------------------------
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Update the ConfidenceMap with the scores
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.. ocv:function:: void TrackerStateEstimator::update( std::vector<ConfidenceMap>& confidenceMaps )
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:param confidenceMaps: The overall appearance model as a list of :c:type:`ConfidenceMap`
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TrackerStateEstimator::getClassName
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-----------------------------------
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Get the name of the specific TrackerStateEstimator
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.. ocv:function:: String TrackerStateEstimator::getClassName() const
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TrackerModel
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------------
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Abstract class that represents the model of the target. It must be instantiated by specialized tracker
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[AAM]_ Ak
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Inherits this with your TrackerModel
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.. ocv:class:: TrackerModel
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TrackerModel class::
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class CV_EXPORTS_W TrackerModel
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{
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public:
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TrackerModel();
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virtual ~TrackerModel();
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void modelEstimation( const std::vector<Mat>& responses );
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void modelUpdate();
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bool runStateEstimator();
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bool setTrackerStateEstimator( Ptr<TrackerStateEstimator> trackerStateEstimator );
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void setLastTargetState( const Ptr<TrackerTargetState>& lastTargetState );
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Ptr<TrackerTargetState> getLastTargetState() const;
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const std::vector<ConfidenceMap>& getConfidenceMaps() const;
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const ConfidenceMap& getLastConfidenceMap() const;
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Ptr<TrackerStateEstimator> getTrackerStateEstimator() const;
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};
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TrackerModel::modelEstimation
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-----------------------------
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Estimate the most likely target location
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[AAM]_ ME, Model Estimation table I
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.. ocv:function:: void TrackerModel::modelEstimation( const std::vector<Mat>& responses )
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:param responses: Features extracted from :ocv:class:`TrackerFeatureSet`
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TrackerModel::modelUpdate
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-------------------------
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Update the model
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[AAM]_ MU, Model Update table I
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.. ocv:function:: void TrackerModel::modelUpdate()
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TrackerModel::runStateEstimator
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-------------------------------
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Run the TrackerStateEstimator, return true if is possible to estimate a new state, false otherwise
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.. ocv:function:: bool TrackerModel::runStateEstimator()
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TrackerModel::setTrackerStateEstimator
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--------------------------------------
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Set TrackerEstimator, return true if the tracker state estimator is added, false otherwise
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.. ocv:function:: bool TrackerModel::setTrackerStateEstimator( Ptr<TrackerStateEstimator> trackerStateEstimator )
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:param trackerStateEstimator: The :ocv:class:`TrackerStateEstimator`
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.. note:: You can add only one :ocv:class:`TrackerStateEstimator`
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TrackerModel::setLastTargetState
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--------------------------------
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Set the current :ocv:class:`TrackerTargetState` in the :c:type:`Trajectory`
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.. ocv:function:: void TrackerModel::setLastTargetState( const Ptr<TrackerTargetState>& lastTargetState )
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:param lastTargetState: The current :ocv:class:`TrackerTargetState`
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TrackerModel::getLastTargetState
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--------------------------------
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Get the last :ocv:class:`TrackerTargetState` from :c:type:`Trajectory`
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.. ocv:function:: Ptr<TrackerTargetState> TrackerModel::getLastTargetState() const
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TrackerModel::getConfidenceMaps
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-------------------------------
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Get the list of the :c:type:`ConfidenceMap`
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.. ocv:function:: const std::vector<ConfidenceMap>& TrackerModel::getConfidenceMaps() const
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TrackerModel::getLastConfidenceMap
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----------------------------------
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Get the last :c:type:`ConfidenceMap` for the current frame
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.. ocv:function:: const ConfidenceMap& TrackerModel::getLastConfidenceMap() const
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TrackerModel::getTrackerStateEstimator
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--------------------------------------
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Get the :ocv:class:`TrackerStateEstimator`
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.. ocv:function:: Ptr<TrackerStateEstimator> TrackerModel::getTrackerStateEstimator() const
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Specialized TrackerStateEstimator
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=================================
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In [AMVOT]_ Statistical modeling (Fig. 3), Table III (generative) - IV (discriminative) - V (hybrid) are described the most known statistical model.
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At moment :ocv:class:`TrackerStateEstimatorMILBoosting` and :ocv:class:`TrackerStateEstimatorAdaBoosting` are implemented.
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TrackerStateEstimatorMILBoosting : TrackerStateEstimator
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--------------------------------------------------------
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TrackerStateEstimator based on Boosting
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.. ocv:class:: TrackerStateEstimatorMILBoosting
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TrackerStateEstimatorMILBoosting class::
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class CV_EXPORTS_W TrackerStateEstimatorMILBoosting : public TrackerStateEstimator
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{
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public:
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class TrackerMILTargetState : public TrackerTargetState
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{
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...
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};
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TrackerStateEstimatorMILBoosting( int nFeatures = 250 );
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~TrackerStateEstimatorMILBoosting();
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void setCurrentConfidenceMap( ConfidenceMap& confidenceMap );
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};
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TrackerMILTargetState : TrackerTargetState
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------------------------------------------
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Implementation of the target state for TrackerMILTargetState
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.. ocv:class:: TrackerMILTargetState
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TrackerMILTargetState class::
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class TrackerMILTargetState : public TrackerTargetState
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{
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public:
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TrackerMILTargetState( const Point2f& position, int targetWidth, int targetHeight, bool foreground, const Mat& features );
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~TrackerMILTargetState(){};
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void setTargetFg( bool foreground );
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void setFeatures( const Mat& features );
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bool isTargetFg() const;
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Mat getFeatures() const;
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};
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TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setTargetFg
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--------------------------------------------------------------------
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Set label: true for target foreground, false for background
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.. ocv:function:: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setTargetFg( bool foreground )
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:param foreground: Label for background/foreground
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TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures
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--------------------------------------------------------------------
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Set the features extracted from :ocv:class:`TrackerFeatureSet`
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.. ocv:function:: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures( const Mat& features )
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:param features: The features extracted
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TrackerStateEstimatorMILBoosting::TrackerMILTargetState::isTargetFg
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-------------------------------------------------------------------
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Get the label. Return true for target foreground, false for background
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.. ocv:function:: bool TrackerStateEstimatorMILBoosting::TrackerMILTargetState::isTargetFg() const
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TrackerStateEstimatorMILBoosting::TrackerMILTargetState::getFeatures
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--------------------------------------------------------------------
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Get the features extracted
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.. ocv:function:: void TrackerStateEstimatorMILBoosting::TrackerMILTargetState::setFeatures( const Mat& features )
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TrackerStateEstimatorMILBoosting::TrackerStateEstimatorMILBoosting
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------------------------------------------------------------------
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Constructor
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.. ocv:function:: TrackerStateEstimatorMILBoosting::TrackerStateEstimatorMILBoosting( int nFeatures=250 )
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:param nFeatures: Number of features for each sample
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TrackerStateEstimatorMILBoosting::setCurrentConfidenceMap
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---------------------------------------------------------
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Set the current confidenceMap
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.. ocv:function:: void TrackerStateEstimatorMILBoosting::setCurrentConfidenceMap( ConfidenceMap& confidenceMap )
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:param confidenceMap: The current :c:type:`ConfidenceMap`
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TrackerStateEstimatorAdaBoosting : TrackerStateEstimator
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--------------------------------------------------------
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TrackerStateEstimatorAdaBoosting based on ADA-Boosting
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.. ocv:class:: TrackerStateEstimatorAdaBoosting
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TrackerStateEstimatorAdaBoosting class::
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class CV_EXPORTS_W TrackerStateEstimatorAdaBoosting : public TrackerStateEstimator
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{
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public:
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class TrackerAdaBoostingTargetState : public TrackerTargetState
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{
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...
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};
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TrackerStateEstimatorAdaBoosting( int numClassifer, int initIterations, int nFeatures, Size patchSize, const Rect& ROI, const std::vector<std::pair<float, float> >& meanSigma );
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~TrackerStateEstimatorAdaBoosting();
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Rect getSampleROI() const;
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void setSampleROI( const Rect& ROI );
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void setCurrentConfidenceMap( ConfidenceMap& confidenceMap );
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std::vector<int> computeSelectedWeakClassifier();
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std::vector<int> computeReplacedClassifier();
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std::vector<int> computeSwappedClassifier();
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void setMeanSigmaPair( const std::vector<std::pair<float, float> >& meanSigmaPair );
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};
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TrackerAdaBoostingTargetState : TrackerTargetState
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--------------------------------------------------
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Implementation of the target state for TrackerAdaBoostingTargetState
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.. ocv:class:: TrackerAdaBoostingTargetState
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TrackerAdaBoostingTargetState class::
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class TrackerAdaBoostingTargetState : public TrackerTargetState
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{
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public:
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TrackerAdaBoostingTargetState( const Point2f& position, int width, int height, bool foreground, const Mat& responses );
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~TrackerAdaBoostingTargetState(){};
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void setTargetResponses( const Mat& responses );
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void setTargetFg( bool foreground );
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Mat getTargetResponses() const;
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bool isTargetFg() const;
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};
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TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetFg
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----------------------------------------------------------------------------
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Set label: true for target foreground, false for background
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.. ocv:function:: void TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetFg( bool foreground )
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:param foreground: Label for background/foreground
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TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetResponses
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-----------------------------------------------------------------------------------
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Set the features extracted from :ocv:class:`TrackerFeatureSet`
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.. ocv:function:: void TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::setTargetResponses( const Mat& responses )
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:param responses: The features extracted
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TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::isTargetFg
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---------------------------------------------------------------------------
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Get the label. Return true for target foreground, false for background
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.. ocv:function:: bool TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::isTargetFg() const
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TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::getTargetResponses
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-----------------------------------------------------------------------------------
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Get the features extracted
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.. ocv:function:: Mat TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState::getTargetResponses()
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TrackerStateEstimatorAdaBoosting::TrackerStateEstimatorAdaBoosting
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------------------------------------------------------------------
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Constructor
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.. ocv:function:: TrackerStateEstimatorAdaBoosting::TrackerStateEstimatorAdaBoosting( int numClassifer, int initIterations, int nFeatures, Size patchSize, const Rect& ROI, const std::vector<std::pair<float, float> >& meanSigma )
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:param numClassifer: Number of base classifiers
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:param initIterations: Number of iterations in the initialization
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:param nFeatures: Number of features/weak classifiers
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:param patchSize: tracking rect
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:param ROI: initial ROI
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:param meanSigma: pairs of mean/sigma
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TrackerStateEstimatorAdaBoosting::setCurrentConfidenceMap
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---------------------------------------------------------
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Set the current confidenceMap
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.. ocv:function:: void TrackerStateEstimatorAdaBoosting::setCurrentConfidenceMap( ConfidenceMap& confidenceMap )
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:param confidenceMap: The current :c:type:`ConfidenceMap`
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TrackerStateEstimatorAdaBoosting::getSampleROI
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----------------------------------------------
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Get the sampling ROI
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.. ocv:function:: Rect TrackerStateEstimatorAdaBoosting::getSampleROI() const
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TrackerStateEstimatorAdaBoosting::setSampleROI
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----------------------------------------------
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Set the sampling ROI
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.. ocv:function:: void TrackerStateEstimatorAdaBoosting::setSampleROI( const Rect& ROI )
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:param ROI: the sampling ROI
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TrackerStateEstimatorAdaBoosting::computeSelectedWeakClassifier
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---------------------------------------------------------------
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Get the list of the selected weak classifiers for the classification step
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.. ocv:function:: std::vector<int> TrackerStateEstimatorAdaBoosting::computeSelectedWeakClassifier()
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TrackerStateEstimatorAdaBoosting::computeReplacedClassifier
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-----------------------------------------------------------
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Get the list of the weak classifiers that should be replaced
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.. ocv:function:: std::vector<int> TrackerStateEstimatorAdaBoosting::computeReplacedClassifier()
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TrackerStateEstimatorAdaBoosting::computeSwappedClassifier
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----------------------------------------------------------
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Get the list of the weak classifiers that replace those to be replaced
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.. ocv:function:: std::vector<int> TrackerStateEstimatorAdaBoosting::computeSwappedClassifier()
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TrackerStateEstimatorAdaBoosting::setMeanSigmaPair
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--------------------------------------------------
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Set the mean/sigma to instantiate possibly new classifiers
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.. ocv:function:: void TrackerStateEstimatorAdaBoosting::setMeanSigmaPair( const std::vector<std::pair<float, float> >& meanSigmaPair )
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:param meanSigmaPair: the mean/sigma pairs
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