OpenCV  3.4.0
Open Source Computer Vision
Classes | Public Member Functions | Static Public Member Functions | List of all members
cv::TrackerGOTURN Class Reference

GOTURN ([76]) is kind of trackers based on Convolutional Neural Networks (CNN). While taking all advantages of CNN trackers, GOTURN is much faster due to offline training without online fine-tuning nature. GOTURN tracker addresses the problem of single target tracking: given a bounding box label of an object in the first frame of the video, we track that object through the rest of the video. NOTE: Current method of GOTURN does not handle occlusions; however, it is fairly robust to viewpoint changes, lighting changes, and deformations. Inputs of GOTURN are two RGB patches representing Target and Search patches resized to 227x227. Outputs of GOTURN are predicted bounding box coordinates, relative to Search patch coordinate system, in format X1,Y1,X2,Y2. Original paper is here: http://davheld.github.io/GOTURN/GOTURN.pdf As long as original authors implementation: https://github.com/davheld/GOTURN#train-the-tracker Implementation of training algorithm is placed in separately here due to 3d-party dependencies: https://github.com/Auron-X/GOTURN_Training_Toolkit GOTURN architecture goturn.prototxt and trained model goturn.caffemodel are accessible on opencv_extra GitHub repository. More...

#include "tracker.hpp"

Inheritance diagram for cv::TrackerGOTURN:
cv::Tracker cv::Algorithm

Classes

struct  Params
 

Public Member Functions

virtual ~TrackerGOTURN ()
 
- Public Member Functions inherited from cv::Tracker
virtual ~Tracker ()
 
bool init (InputArray image, const Rect2d &boundingBox)
 Initialize the tracker with a know bounding box that surrounding the target. More...
 
virtual void read (const FileNode &fn)=0
 Reads algorithm parameters from a file storage. More...
 
bool update (InputArray image, Rect2d &boundingBox)
 Update the tracker, find the new most likely bounding box for the target. More...
 
virtual void write (FileStorage &fs) const =0
 Stores algorithm parameters in a file storage. More...
 
- Public Member Functions inherited from cv::Algorithm
 Algorithm ()
 
virtual ~Algorithm ()
 
virtual void clear ()
 Clears the algorithm state. More...
 
virtual bool empty () const
 Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
 
virtual String getDefaultName () const
 
virtual void save (const String &filename) const
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
 

Static Public Member Functions

static Ptr< TrackerGOTURNcreate (const TrackerGOTURN::Params &parameters)
 Constructor. More...
 
static Ptr< TrackerGOTURNcreate ()
 
- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp > load (const String &filename, const String &objname=String())
 Loads algorithm from the file. More...
 
template<typename _Tp >
static Ptr< _Tp > loadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String. More...
 
template<typename _Tp >
static Ptr< _Tp > read (const FileNode &fn)
 Reads algorithm from the file node. More...
 

Additional Inherited Members

- Protected Member Functions inherited from cv::Tracker
virtual bool initImpl (const Mat &image, const Rect2d &boundingBox)=0
 
virtual bool updateImpl (const Mat &image, Rect2d &boundingBox)=0
 
- Protected Member Functions inherited from cv::Algorithm
void writeFormat (FileStorage &fs) const
 
- Protected Attributes inherited from cv::Tracker
Ptr< TrackerFeatureSetfeatureSet
 
bool isInit
 
Ptr< TrackerModelmodel
 
Ptr< TrackerSamplersampler
 

Detailed Description

GOTURN ([76]) is kind of trackers based on Convolutional Neural Networks (CNN). While taking all advantages of CNN trackers, GOTURN is much faster due to offline training without online fine-tuning nature. GOTURN tracker addresses the problem of single target tracking: given a bounding box label of an object in the first frame of the video, we track that object through the rest of the video. NOTE: Current method of GOTURN does not handle occlusions; however, it is fairly robust to viewpoint changes, lighting changes, and deformations. Inputs of GOTURN are two RGB patches representing Target and Search patches resized to 227x227. Outputs of GOTURN are predicted bounding box coordinates, relative to Search patch coordinate system, in format X1,Y1,X2,Y2. Original paper is here: http://davheld.github.io/GOTURN/GOTURN.pdf As long as original authors implementation: https://github.com/davheld/GOTURN#train-the-tracker Implementation of training algorithm is placed in separately here due to 3d-party dependencies: https://github.com/Auron-X/GOTURN_Training_Toolkit GOTURN architecture goturn.prototxt and trained model goturn.caffemodel are accessible on opencv_extra GitHub repository.

Constructor & Destructor Documentation

§ ~TrackerGOTURN()

virtual cv::TrackerGOTURN::~TrackerGOTURN ( )
inlinevirtual

Member Function Documentation

§ create() [1/2]

static Ptr<TrackerGOTURN> cv::TrackerGOTURN::create ( const TrackerGOTURN::Params parameters)
static
Python:
retval=cv.TrackerGOTURN_create()

Constructor.

Parameters
parametersGOTURN parameters TrackerGOTURN::Params

§ create() [2/2]

static Ptr<TrackerGOTURN> cv::TrackerGOTURN::create ( )
static
Python:
retval=cv.TrackerGOTURN_create()

The documentation for this class was generated from the following file: