OpenCV  3.4.1
Open Source Computer Vision
Classes | Namespaces | Typedefs
tracker.hpp File Reference
#include "opencv2/core.hpp"
#include "opencv2/imgproc/types_c.h"
#include "feature.hpp"
#include "onlineMIL.hpp"
#include "onlineBoosting.hpp"

Classes

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::TrackerCSRT::Params
 
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::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::TrackerCSRT
 Discriminative Correlation Filter Tracker with Channel and Spatial Reliability. 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 ([77]) 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 [78] which is extended to KCF with color-names features ([37]). The original paper of KCF is available at http://www.robots.ox.ac.uk/~joao/publications/henriques_tpami2015.pdf 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::TrackerMOSSE
 the MOSSE tracker note, that this tracker works with grayscale images, if passed bgr ones, they will get converted internally. [16] Visual Object Tracking using Adaptive Correlation Filters 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
 

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...