tracker.hpp File Reference

#include "opencv2/core.hpp"
#include "opencv2/imgproc/types_c.h"
#include "feature.hpp"
#include "onlineMIL.hpp"
#include "onlineBoosting.hpp"
#include <iostream>

Classes
struct	cv::ROISelector::handlerT
class	cv::MultiTracker
	This class is used to track multiple objects using the specified tracker algorithm. The MultiTracker is naive implementation of multiple object tracking. It process the tracked objects independently without any optimization accross the tracked objects. More...
class	cv::MultiTracker_Alt
	Base abstract class for the long-term Multi Object Trackers: More...
class	cv::MultiTrackerTLD
	Multi Object Tracker for TLD. TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. More...
struct	cv::TrackerGOTURN::Params
struct	cv::TrackerKCF::Params
struct	cv::TrackerTLD::Params
struct	cv::TrackerMedianFlow::Params
struct	cv::TrackerBoosting::Params
struct	cv::TrackerMIL::Params
struct	cv::TrackerFeatureHAAR::Params
struct	cv::TrackerSamplerPF::Params
	This structure contains all the parameters that can be varied during the course of sampling algorithm. Below is the structure exposed, together with its members briefly explained with reference to the above discussion on algorithm's working. More...
struct	cv::TrackerSamplerCS::Params
struct	cv::TrackerSamplerCSC::Params
class	cv::ROISelector
class	cv::Tracker
	Base abstract class for the long-term tracker: More...
class	cv::TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState
	Implementation of the target state for TrackerAdaBoostingTargetState. More...
class	cv::TrackerBoosting
	This is a real-time object tracking based on a novel on-line version of the AdaBoost algorithm. More...
class	cv::TrackerFeature
	Abstract base class for TrackerFeature that represents the feature. More...
class	cv::TrackerFeatureFeature2d
	TrackerFeature based on Feature2D. More...
class	cv::TrackerFeatureHAAR
	TrackerFeature based on HAAR features, used by TrackerMIL and many others algorithms. More...
class	cv::TrackerFeatureHOG
	TrackerFeature based on HOG. More...
class	cv::TrackerFeatureLBP
	TrackerFeature based on LBP. More...
class	cv::TrackerFeatureSet
	Class that manages the extraction and selection of features. More...
class	cv::TrackerGOTURN
	GOTURN ([70]) 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...
class	cv::TrackerKCF
	KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed. This tracking method is an implementation of [71] which is extended to KFC with color-names features ([34]). The original paper of KCF is available at http://home.isr.uc.pt/~henriques/circulant/index.html as well as the matlab implementation. For more information about KCF with color-names features, please refer to http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html. More...
class	cv::TrackerMedianFlow
	Median Flow tracker implementation. More...
class	cv::TrackerMIL
	The MIL algorithm trains a classifier in an online manner to separate the object from the background. More...
class	cv::TrackerStateEstimatorMILBoosting::TrackerMILTargetState
class	cv::TrackerModel
	Abstract class that represents the model of the target. It must be instantiated by specialized tracker. More...
class	cv::TrackerSampler
	Class that manages the sampler in order to select regions for the update the model of the tracker [AAM] Sampling e Labeling. See table I and section III B. More...
class	cv::TrackerSamplerAlgorithm
	Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler. More...
class	cv::TrackerSamplerCS
	TrackerSampler based on CS (current state), used by algorithm TrackerBoosting. More...
class	cv::TrackerSamplerCSC
	TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL. More...
class	cv::TrackerSamplerPF
	This sampler is based on particle filtering. More...
class	cv::TrackerStateEstimator
	Abstract base class for TrackerStateEstimator that estimates the most likely target state. More...
class	cv::TrackerStateEstimatorAdaBoosting
	TrackerStateEstimatorAdaBoosting based on ADA-Boosting. More...
class	cv::TrackerStateEstimatorMILBoosting
	TrackerStateEstimator based on Boosting. More...
class	cv::TrackerStateEstimatorSVM
	TrackerStateEstimator based on SVM. More...
class	cv::TrackerTargetState
	Abstract base class for TrackerTargetState that represents a possible state of the target. More...
class	cv::TrackerTLD
	TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. More...

Namespaces
	cv

Macros
#define	BOILERPLATE_CODE(name, classname)

Typedefs
typedef std::vector< std::pair< Ptr< TrackerTargetState >, float > >	cv::ConfidenceMap
	Represents the model of the target at frame \(k\) (all states and scores) More...
typedef std::vector< Ptr< TrackerTargetState > >	cv::Trajectory
	Represents the estimate states for all frames. More...

Functions
Rect2d	cv::selectROI (Mat img, bool fromCenter=true)
Rect2d	cv::selectROI (const cv::String &windowName, Mat img, bool showCrossair=true, bool fromCenter=true)
void	cv::selectROI (const cv::String &windowName, Mat img, std::vector< Rect2d > &boundingBox, bool fromCenter=true)

Macro Definition Documentation

BOILERPLATE_CODE

#define BOILERPLATE_CODE	(		name,
			classname
	)

Value:

static Ptr<classname> createTracker(const classname::Params &parameters=classname::Params());\
    virtual ~classname(){};