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This contribution aims to implement the sampler based on particle filtering within a generic tracking API that opencv has. It still remains to write the documentation.
122 lines
3.7 KiB
ReStructuredText
122 lines
3.7 KiB
ReStructuredText
Tracker Algorithms
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==================
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.. highlight:: cpp
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The following algorithms are implemented at the moment.
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.. [MIL] B Babenko, M-H Yang, and S Belongie, Visual Tracking with Online Multiple Instance Learning, In CVPR, 2009
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.. [OLB] H Grabner, M Grabner, and H Bischof, Real-time tracking via on-line boosting, In Proc. BMVC, volume 1, pages 47– 56, 2006
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TrackerBoosting
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---------------
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This is a real-time object tracking based on a novel on-line version of the AdaBoost algorithm.
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The classifier uses the surrounding background as negative examples in update step to avoid the drifting problem. The implementation is based on
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[OLB]_.
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.. ocv:class:: TrackerBoosting
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Implementation of TrackerBoosting from :ocv:class:`Tracker`::
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class CV_EXPORTS_W TrackerBoosting : public Tracker
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{
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public:
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TrackerBoosting( const TrackerBoosting::Params ¶meters = TrackerBoosting::Params() );
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virtual ~TrackerBoosting();
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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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TrackerBoosting::Params
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-----------------------------------------------------------------------
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.. ocv:struct:: TrackerBoosting::Params
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List of BOOSTING parameters::
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struct CV_EXPORTS Params
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{
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Params();
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int numClassifiers; //the number of classifiers to use in a OnlineBoosting algorithm
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float samplerOverlap; //search region parameters to use in a OnlineBoosting algorithm
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float samplerSearchFactor; // search region parameters to use in a OnlineBoosting algorithm
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int iterationInit; //the initial iterations
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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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TrackerBoosting::TrackerBoosting
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-----------------------------------------------------------------------
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Constructor
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.. ocv:function:: bool TrackerBoosting::TrackerBoosting( const TrackerBoosting::Params ¶meters = TrackerBoosting::Params() )
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:param parameters: BOOSTING parameters :ocv:struct:`TrackerBoosting::Params`
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TrackerMIL
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----------
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The MIL algorithm trains a classifier in an online manner to separate the object from the background. Multiple Instance Learning avoids the drift problem for a robust tracking. The implementation is based on [MIL]_.
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Original code can be found here http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml
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.. ocv:class:: TrackerMIL
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Implementation of TrackerMIL from :ocv:class:`Tracker`::
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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 ¶meters = TrackerMIL::Params() );
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virtual ~TrackerMIL();
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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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TrackerMIL::Params
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------------------
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.. ocv:struct:: TrackerMIL::Params
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List of MIL parameters::
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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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TrackerMIL::TrackerMIL
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----------------------
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Constructor
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.. ocv:function:: bool TrackerMIL::TrackerMIL( const TrackerMIL::Params ¶meters = TrackerMIL::Params() )
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:param parameters: MIL parameters :ocv:struct:`TrackerMIL::Params`
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