Tracker Algorithms ================== .. highlight:: cpp The following algorithms are implemented at the moment. .. [MIL] B Babenko, M-H Yang, and S Belongie, Visual Tracking with Online Multiple Instance Learning, In CVPR, 2009 .. [OLB] H Grabner, M Grabner, and H Bischof, Real-time tracking via on-line boosting, In Proc. BMVC, volume 1, pages 47– 56, 2006 .. [MedianFlow] Z. Kalal, K. Mikolajczyk, and J. Matas, “Forward-Backward Error: Automatic Detection of Tracking Failures,” International Conference on Pattern Recognition, 2010, pp. 23-26. .. [TLD] Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-Learning-Detection,” Pattern Analysis and Machine Intelligence 2011. TrackerBoosting --------------- This is a real-time object tracking based on a novel on-line version of the AdaBoost algorithm. The classifier uses the surrounding background as negative examples in update step to avoid the drifting problem. The implementation is based on [OLB]_. .. ocv:class:: TrackerBoosting Implementation of TrackerBoosting from :ocv:class:`Tracker`:: class CV_EXPORTS_W TrackerBoosting : public Tracker { public: void read( const FileNode& fn ); void write( FileStorage& fs ) const; static Ptr createTracker(const trackerBoosting::Params ¶meters=trackerBoosting::Params()); virtual ~trackerBoosting(){}; protected: bool initImpl( const Mat& image, const Rect2d& boundingBox ); bool updateImpl( const Mat& image, Rect2d& boundingBox ); }; TrackerBoosting::Params ----------------------------------------------------------------------- .. ocv:struct:: TrackerBoosting::Params List of BOOSTING parameters:: struct CV_EXPORTS Params { Params(); int numClassifiers; //the number of classifiers to use in a OnlineBoosting algorithm float samplerOverlap; //search region parameters to use in a OnlineBoosting algorithm float samplerSearchFactor; // search region parameters to use in a OnlineBoosting algorithm int iterationInit; //the initial iterations int featureSetNumFeatures; // #features void read( const FileNode& fn ); void write( FileStorage& fs ) const; }; TrackerBoosting::createTracker ----------------------------------------------------------------------- Constructor .. ocv:function:: Ptr TrackerBoosting::createTracker(const trackerBoosting::Params ¶meters=trackerBoosting::Params()) :param parameters: BOOSTING parameters :ocv:struct:`TrackerBoosting::Params` TrackerMIL ---------------------- 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]_. Original code can be found here http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml .. ocv:class:: TrackerMIL Implementation of TrackerMIL from :ocv:class:`Tracker`:: class CV_EXPORTS_W TrackerMIL : public Tracker { public: void read( const FileNode& fn ); void write( FileStorage& fs ) const; static Ptr createTracker(const trackerMIL::Params ¶meters=trackerMIL::Params()); virtual ~trackerMIL(){}; protected: bool initImpl( const Mat& image, const Rect2d& boundingBox ); bool updateImpl( const Mat& image, Rect2d& boundingBox ); }; TrackerMIL::Params ------------------ .. ocv:struct:: TrackerMIL::Params List of MIL parameters:: struct CV_EXPORTS Params { Params(); //parameters for sampler float samplerInitInRadius; // radius for gathering positive instances during init int samplerInitMaxNegNum; // # negative samples to use during init float samplerSearchWinSize; // size of search window float samplerTrackInRadius; // radius for gathering positive instances during tracking int samplerTrackMaxPosNum; // # positive samples to use during tracking int samplerTrackMaxNegNum; // # negative samples to use during tracking int featureSetNumFeatures; // # features void read( const FileNode& fn ); void write( FileStorage& fs ) const; }; TrackerMIL::createTracker ------------------------------- Constructor .. ocv:function:: Ptr TrackerMIL::createTracker(const trackerMIL::Params ¶meters=trackerMIL::Params()) :param parameters: MIL parameters :ocv:struct:`TrackerMIL::Params` TrackerMedianFlow ---------------------- Implementation of a paper "Forward-Backward Error: Automatic Detection of Tracking Failures" by Z. Kalal, K. Mikolajczyk and Jiri Matas. The implementation is based on [MedianFlow]_. The tracker is suitable for very smooth and predictable movements when object is visible throughout the whole sequence. It's quite and accurate for this type of problems (in particular, it was shown by authors to outperform MIL). During the implementation period the code at http://www.aonsquared.co.uk/node/5, the courtesy of the author Arthur Amarra, was used for the reference purpose. .. ocv:class:: TrackerMedianFlow Implementation of TrackerMedianFlow from :ocv:class:`Tracker`:: class CV_EXPORTS_W TrackerMedianFlow : public Tracker { public: void read( const FileNode& fn ); void write( FileStorage& fs ) const; static Ptr createTracker(const trackerMedianFlow::Params ¶meters=trackerMedianFlow::Params()); virtual ~trackerMedianFlow(){}; protected: bool initImpl( const Mat& image, const Rect2d& boundingBox ); bool updateImpl( const Mat& image, Rect2d& boundingBox ); }; TrackerMedianFlow::Params ------------------------------------ .. ocv:struct:: TrackerMedianFlow::Params List of MedianFlow parameters:: struct CV_EXPORTS Params { Params(); int pointsInGrid; //square root of number of keypoints used; increase it to trade //accurateness for speed; default value is sensible and recommended void read( const FileNode& fn ); void write( FileStorage& fs ) const; }; TrackerMedianFlow::createTracker ----------------------------------- Constructor .. ocv:function:: Ptr TrackerMedianFlow::createTracker(const trackerMedianFlow::Params ¶meters=trackerMedianFlow::Params()) :param parameters: Median Flow parameters :ocv:struct:`TrackerMedianFlow::Params` TrackerTLD ---------------------- TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates detector’s errors and updates it to avoid these errors in the future. The implementation is based on [TLD]_. The Median Flow algorithm (see above) was chosen as a tracking component in this implementation, following authors. Tracker is supposed to be able to handle rapid motions, partial occlusions, object absence etc. .. ocv:class:: TrackerTLD Implementation of TrackerTLD from :ocv:class:`Tracker`:: class CV_EXPORTS_W TrackerTLD : public Tracker { public: void read( const FileNode& fn ); void write( FileStorage& fs ) const; static Ptr createTracker(const trackerTLD::Params ¶meters=trackerTLD::Params()); virtual ~trackerTLD(){}; protected: bool initImpl( const Mat& image, const Rect2d& boundingBox ); bool updateImpl( const Mat& image, Rect2d& boundingBox ); }; TrackerTLD::Params ------------------------ .. ocv:struct:: TrackerTLD::Params List of TLD parameters:: struct CV_EXPORTS Params { Params(); void read( const FileNode& fn ); void write( FileStorage& fs ) const; }; TrackerTLD::createTracker ------------------------------- Constructor .. ocv:function:: Ptr TrackerTLD::createTracker(const trackerTLD::Params ¶meters=trackerTLD::Params()) :param parameters: TLD parameters :ocv:struct:`TrackerTLD::Params`