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Added tracking module
Modified Copyrights Moved plantuml source files under doc disabled tests Added include of precomp.hpp
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‎modules/tracking/CMakeLists.txt‎

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set(the_description "Tracking API")
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ocv_define_module(tracking opencv_imgproc)

‎modules/tracking/doc/common_interfaces_tracker.rst‎

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Common Interfaces of Tracker
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============================
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.. highlight:: cpp
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Tracker : Algorithm
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-------------------
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.. ocv:class:: Tracker
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Base abstract class for the long-term tracker::
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class CV_EXPORTS_W Tracker : public virtual Algorithm
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{
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virtual ~Tracker();
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bool init( const Mat& image, const Rect& boundingBox );
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bool update( const Mat& image, Rect& boundingBox );
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static Ptr<Tracker> create( const String& trackerType );
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};
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Tracker::init
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-------------
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Initialize the tracker with a know bounding box that surrounding the target
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.. ocv:function:: bool Tracker::init( const Mat& image, const Rect& boundingBox )
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:param image: The initial frame
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:param boundingBox: The initial boundig box
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Tracker::update
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---------------
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Update the tracker, find the new most likely bounding box for the target
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.. ocv:function:: bool Tracker::update( const Mat& image, Rect& boundingBox )
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:param image: The current frame
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:param boundingBox: The boundig box that represent the new target location
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Tracker::create
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---------------
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Creates a tracker by its name.
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.. ocv:function:: static Ptr<Tracker> Tracker::create( const String& trackerType )
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:param trackerType: Tracker type
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The following detector types are supported:
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* ``"MIL"`` -- :ocv:class:`TrackerMIL`
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* ``"BOOSTING"`` -- :ocv:class:`TrackerBoosting`
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Creating Own Tracker
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--------------------
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If you want create a new tracker, you should follow some simple rules.
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First, your tracker should be inherit from :ocv:class:`Tracker`, so you must implement two method:
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* Tracker: initImpl, it should be called once in the first frame, here you should initialize all structures. The second argument is the initial bounding box of the target.
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* Tracker:updateImpl, it should be called at the begin of in loop through video frames. Here you should overwrite the bounding box with new location.
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Example of creating specialized Tracker ``TrackerMIL`` : ::
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class CV_EXPORTS_W TrackerMIL : public Tracker
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{
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public:
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TrackerMIL( const TrackerMIL::Params &parameters = TrackerMIL::Params() );
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virtual ~TrackerMIL();
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...
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protected:
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bool initImpl( const Mat& image, const Rect& boundingBox );
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bool updateImpl( const Mat& image, Rect& boundingBox );
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...
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};
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Every tracker has three component :ocv:class:`TrackerSampler`, :ocv:class:`TrackerFeatureSet` and :ocv:class:`TrackerModel`.
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The first two are instantiated from Tracker base class, instead the last component is abstract, so you must implement your TrackerModel.
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Finally add your tracker in the file tracking_init.cpp
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TrackerSampler
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..............
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TrackerSampler is already instantiated, but you should define the sampling algorithm and add the classes (or single class) to TrackerSampler.
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You can choose one of the ready implementation as TrackerSamplerCSC or you can implement your sampling method, in this case
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the class must inherit :ocv:class:`TrackerSamplerAlgorithm`. Fill the samplingImpl method that writes the result in "sample" output argument.
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Example of creating specialized TrackerSamplerAlgorithm ``TrackerSamplerCSC`` : ::
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class CV_EXPORTS_W TrackerSamplerCSC : public TrackerSamplerAlgorithm
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{
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public:
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TrackerSamplerCSC( const TrackerSamplerCSC::Params &parameters = TrackerSamplerCSC::Params() );
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~TrackerSamplerCSC();
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...
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protected:
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bool samplingImpl( const Mat& image, Rect boundingBox, std::vector<Mat>& sample );
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...
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};
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Example of adding TrackerSamplerAlgorithm to TrackerSampler : ::
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//sampler is the TrackerSampler
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Ptr<TrackerSamplerAlgorithm> CSCSampler = new TrackerSamplerCSC( CSCparameters );
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if( !sampler->addTrackerSamplerAlgorithm( CSCSampler ) )
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return false;
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//or add CSC sampler with default parameters
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//sampler->addTrackerSamplerAlgorithm( "CSC" );
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.. seealso::
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:ocv:class:`TrackerSamplerCSC`, :ocv:class:`TrackerSamplerAlgorithm`
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TrackerFeatureSet
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.................
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TrackerFeatureSet is already instantiated (as first) , but you should define what kinds of features you'll use in your tracker.
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You can use multiple feature types, so you can add a ready implementation as :ocv:class:`TrackerFeatureHAAR` in your TrackerFeatureSet or develop your own implementation.
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In this case, in the computeImpl method put the code that extract the features and
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in the selection method optionally put the code for the refinement and selection of the features.
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Example of creating specialized TrackerFeature ``TrackerFeatureHAAR`` : ::
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class CV_EXPORTS_W TrackerFeatureHAAR : public TrackerFeature
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{
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public:
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TrackerFeatureHAAR( const TrackerFeatureHAAR::Params &parameters = TrackerFeatureHAAR::Params() );
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~TrackerFeatureHAAR();
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void selection( Mat& response, int npoints );
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...
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protected:
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bool computeImpl( const std::vector<Mat>& images, Mat& response );
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...
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};
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Example of adding TrackerFeature to TrackerFeatureSet : ::
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//featureSet is the TrackerFeatureSet
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Ptr<TrackerFeature> trackerFeature = new TrackerFeatureHAAR( HAARparameters );
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featureSet->addTrackerFeature( trackerFeature );
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.. seealso::
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:ocv:class:`TrackerFeatureHAAR`, :ocv:class:`TrackerFeatureSet`
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TrackerModel
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............
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TrackerModel is abstract, so in your implementation you must develop your TrackerModel that inherit from :ocv:class:`TrackerModel`.
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Fill the method for the estimation of the state "modelEstimationImpl", that estimates the most likely target location,
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see [AAM]_ table I (ME) for further information. Fill "modelUpdateImpl" in order to update the model, see [AAM]_ table I (MU).
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In this class you can use the :c:type:`ConfidenceMap` and :c:type:`Trajectory` to storing the model. The first represents the model on the all
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possible candidate states and the second represents the list of all estimated states.
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Example of creating specialized TrackerModel ``TrackerMILModel`` : ::
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class TrackerMILModel : public TrackerModel
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{
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public:
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TrackerMILModel( const Rect& boundingBox );
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~TrackerMILModel();
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...
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protected:
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void modelEstimationImpl( const std::vector<Mat>& responses );
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void modelUpdateImpl();
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...
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};
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And add it in your Tracker : ::
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bool TrackerMIL::initImpl( const Mat& image, const Rect& boundingBox )
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{
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...
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//model is the general TrackerModel field od the general Tracker
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model = new TrackerMILModel( boundingBox );
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...
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}
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In the last step you should define the TrackerStateEstimator based on your implementation or you can use one of ready class as :ocv:class:`TrackerStateEstimatorMILBoosting`.
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It represent the statistical part of the model that estimates the most likely target state.
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Example of creating specialized TrackerStateEstimator ``TrackerStateEstimatorMILBoosting`` : ::
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class CV_EXPORTS_W TrackerStateEstimatorMILBoosting : public TrackerStateEstimator
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{
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class TrackerMILTargetState : public TrackerTargetState
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{
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...
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};
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public:
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TrackerStateEstimatorMILBoosting( int nFeatures = 250 );
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~TrackerStateEstimatorMILBoosting();
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...
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protected:
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Ptr<TrackerTargetState> estimateImpl( const std::vector<ConfidenceMap>& confidenceMaps );
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void updateImpl( std::vector<ConfidenceMap>& confidenceMaps );
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...
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};
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And add it in your TrackerModel : ::
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//model is the TrackerModel of your Tracker
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Ptr<TrackerStateEstimatorMILBoosting> stateEstimator = new TrackerStateEstimatorMILBoosting( params.featureSetNumFeatures );
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model->setTrackerStateEstimator( stateEstimator );
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.. seealso::
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:ocv:class:`TrackerModel`, :ocv:class:`TrackerStateEstimatorMILBoosting`, :ocv:class:`TrackerTargetState`
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During this step, you should define your TrackerTargetState based on your implementation. :ocv:class:`TrackerTargetState` base class has only the bounding box (upper-left position, width and height), you can
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enrich it adding scale factor, target rotation, etc.
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Example of creating specialized TrackerTargetState ``TrackerMILTargetState`` : ::
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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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...
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private:
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bool isTarget;
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Mat targetFeatures;
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...
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};
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Try it
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......
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To try your tracker you can use the demo at https://github.com/lenlen/opencv/blob/tracking_api/samples/cpp/tracker.cpp.
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The first argument is the name of the tracker and the second is a video source.

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