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tracking: move/copy files before modification
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Alexander Alekhin
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// By downloading, copying, installing or using the software you agree to this license.
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// License Agreement
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// For Open Source Computer Vision Library
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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//
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//M*/
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#ifndef __OPENCV_TRACKING_HPP__
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#define __OPENCV_TRACKING_HPP__
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#include "opencv2/core/cvdef.h"
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/** @defgroup tracking Tracking API
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Long-term optical tracking API
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------------------------------
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Long-term optical tracking is an important issue for many computer vision applications in
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real world scenario. The development in this area is very fragmented and this API is an unique
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interface useful for plug several algorithms and compare them. This work is partially based on
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@cite AAM and @cite AMVOT .
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These algorithms start from a bounding box of the target and with their internal representation they
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avoid the drift during the tracking. These long-term trackers are able to evaluate online the
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quality of the location of the target in the new frame, without ground truth.
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There are three main components: the TrackerSampler, the TrackerFeatureSet and the TrackerModel. The
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first component is the object that computes the patches over the frame based on the last target
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location. The TrackerFeatureSet is the class that manages the Features, is possible plug many kind
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of these (HAAR, HOG, LBP, Feature2D, etc). The last component is the internal representation of the
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target, it is the appearance model. It stores all state candidates and compute the trajectory (the
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most likely target states). The class TrackerTargetState represents a possible state of the target.
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The TrackerSampler and the TrackerFeatureSet are the visual representation of the target, instead
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the TrackerModel is the statistical model.
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A recent benchmark between these algorithms can be found in @cite OOT
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Creating Your Own %Tracker
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--------------------
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If you want to create a new tracker, here's what you have to do. First, decide on the name of the class
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for the tracker (to meet the existing style, we suggest something with prefix "tracker", e.g.
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trackerMIL, trackerBoosting) -- we shall refer to this choice as to "classname" in subsequent.
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- Declare your tracker in modules/tracking/include/opencv2/tracking/tracker.hpp. Your tracker should inherit from
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Tracker (please, see the example below). You should declare the specialized Param structure,
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where you probably will want to put the data, needed to initialize your tracker. You should
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get something similar to :
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@code
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class CV_EXPORTS_W TrackerMIL : public Tracker
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{
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public:
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struct CV_EXPORTS Params
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{
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Params();
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//parameters for sampler
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float samplerInitInRadius; // radius for gathering positive instances during init
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int samplerInitMaxNegNum; // # negative samples to use during init
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float samplerSearchWinSize; // size of search window
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float samplerTrackInRadius; // radius for gathering positive instances during tracking
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int samplerTrackMaxPosNum; // # positive samples to use during tracking
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int samplerTrackMaxNegNum; // # negative samples to use during tracking
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int featureSetNumFeatures; // #features
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void read( const FileNode& fn );
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void write( FileStorage& fs ) const;
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};
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@endcode
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of course, you can also add any additional methods of your choice. It should be pointed out,
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however, that it is not expected to have a constructor declared, as creation should be done via
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the corresponding create() method.
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- Finally, you should implement the function with signature :
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@code
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Ptr<classname> classname::create(const classname::Params ¶meters){
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...
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}
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@endcode
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That function can (and probably will) return a pointer to some derived class of "classname",
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which will probably have a real constructor.
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Every tracker has three component TrackerSampler, TrackerFeatureSet and TrackerModel. The first two
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are instantiated from Tracker base class, instead the last component is abstract, so you must
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implement your TrackerModel.
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### TrackerSampler
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TrackerSampler is already instantiated, but you should define the sampling algorithm and add the
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classes (or single class) to TrackerSampler. You can choose one of the ready implementation as
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TrackerSamplerCSC or you can implement your sampling method, in this case the class must inherit
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TrackerSamplerAlgorithm. Fill the samplingImpl method that writes the result in "sample" output
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argument.
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Example of creating specialized TrackerSamplerAlgorithm TrackerSamplerCSC : :
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@code
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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 ¶meters = 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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@endcode
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Example of adding TrackerSamplerAlgorithm to TrackerSampler : :
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@code
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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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@endcode
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@sa
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TrackerSamplerCSC, TrackerSamplerAlgorithm
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### TrackerFeatureSet
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TrackerFeatureSet is already instantiated (as first) , but you should define what kinds of features
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you'll use in your tracker. You can use multiple feature types, so you can add a ready
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implementation as 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 in the selection
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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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@code
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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 ¶meters = 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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@endcode
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Example of adding TrackerFeature to TrackerFeatureSet : :
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@code
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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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@endcode
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@sa
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TrackerFeatureHAAR, TrackerFeatureSet
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### TrackerModel
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TrackerModel is abstract, so in your implementation you must develop your TrackerModel that inherit
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from TrackerModel. Fill the method for the estimation of the state "modelEstimationImpl", that
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estimates the most likely target location, see @cite AAM table I (ME) for further information. Fill
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"modelUpdateImpl" in order to update the model, see @cite AAM table I (MU). In this class you can use
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the :cConfidenceMap and :cTrajectory 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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@code
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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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@endcode
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And add it in your Tracker : :
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@code
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bool TrackerMIL::initImpl( const Mat& image, const Rect2d& boundingBox )
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{
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...
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//model is the general TrackerModel field of the general Tracker
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model = new TrackerMILModel( boundingBox );
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...
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}
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@endcode
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In the last step you should define the TrackerStateEstimator based on your implementation or you can
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use one of ready class as TrackerStateEstimatorMILBoosting. It represent the statistical part of the
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model that estimates the most likely target state.
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Example of creating specialized TrackerStateEstimator TrackerStateEstimatorMILBoosting : :
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@code
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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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@endcode
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And add it in your TrackerModel : :
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@code
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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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@endcode
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@sa
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TrackerModel, TrackerStateEstimatorMILBoosting, TrackerTargetState
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During this step, you should define your TrackerTargetState based on your implementation.
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TrackerTargetState base class has only the bounding box (upper-left position, width and height), you
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can enrich it adding scale factor, target rotation, etc.
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Example of creating specialized TrackerTargetState TrackerMILTargetState : :
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@code
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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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@endcode
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*/
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#include <opencv2/tracking/tracker.hpp>
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#include <opencv2/tracking/tldDataset.hpp>
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#endif //__OPENCV_TRACKING_HPP__
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1548
modules/tracking/include/opencv2/tracking/tracking_legacy.hpp
Normal file
1548
modules/tracking/include/opencv2/tracking/tracking_legacy.hpp
Normal file
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