docs

modules/tracking/doc/tracker_algorithms.rst

@@ -9,6 +9,10 @@ The following algorithms are implemented at the moment.
 
 .. [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
 ---------------
 
@@ -63,7 +67,7 @@ Constructor
     :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]_.
 
@@ -118,3 +122,108 @@ Constructor
 .. ocv:function:: Ptr<trackerMIL> TrackerMIL::createTracker(const trackerMIL::Params &parameters=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<trackerMedianFlow> createTracker(const trackerMedianFlow::Params &parameters=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> TrackerMedianFlow::createTracker(const trackerMedianFlow::Params &parameters=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<trackerTLD> createTracker(const trackerTLD::Params &parameters=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> TrackerTLD::createTracker(const trackerTLD::Params &parameters=trackerTLD::Params())
+
+    :param parameters: TLD parameters :ocv:struct:`TrackerTLD::Params`

modules/tracking/include/opencv2/tracking/tracker.hpp

@@ -1017,7 +1017,8 @@ class CV_EXPORTS_W TrackerMedianFlow : public Tracker
   struct CV_EXPORTS Params
   {
     Params();
-    int pointsInGrid;
+    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;
   };