OpenCV  4.1.0
Open Source Computer Vision
Classes | Macros | Typedefs | Enumerations | Functions
Tracking API

Classes

class  cv::BaseClassifier
 
class  cv::ClassifierThreshold
 
class  cv::ClfMilBoost
 
class  cv::ClfOnlineStump
 
class  cv::CvFeatureEvaluator
 
class  cv::CvFeatureParams
 
class  cv::CvHaarEvaluator
 
class  cv::CvHaarFeatureParams
 
class  cv::CvHOGEvaluator
 
struct  cv::CvHOGFeatureParams
 
class  cv::CvLBPEvaluator
 
struct  cv::CvLBPFeatureParams
 
class  cv::CvParams
 
class  cv::Detector
 
class  cv::EstimatedGaussDistribution
 
class  cv::CvLBPEvaluator::Feature
 
class  cv::CvHOGEvaluator::Feature
 
class  cv::CvHaarEvaluator::FeatureHaar
 
class  cv::MultiTracker
 This class is used to track multiple objects using the specified tracker algorithm. More...
 
class  cv::MultiTracker_Alt
 Base abstract class for the long-term Multi Object Trackers: More...
 
class  cv::MultiTrackerTLD
 Multi Object Tracker for TLD. 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
 
struct  cv::ClfMilBoost::Params
 
class  cv::StrongClassifierDirectSelection
 
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
 the Boosting tracker More...
 
class  cv::TrackerCSRT
 the CSRT tracker 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
 the GOTURN (Generic Object Tracking Using Regression Networks) tracker More...
 
class  cv::TrackerKCF
 the KCF (Kernelized Correlation Filter) tracker More...
 
class  cv::TrackerMedianFlow
 the Median Flow tracker 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 (Minimum Output Sum of Squared Error) 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
 the TLD (Tracking, learning and detection) tracker More...
 
class  cv::WeakClassifierHaarFeature
 

Macros

#define CC_FEATURE_PARAMS   "featureParams"
 
#define CC_FEATURE_SIZE   "featSize"
 
#define CC_FEATURES   FEATURES
 
#define CC_ISINTEGRAL   "isIntegral"
 
#define CC_MAX_CAT_COUNT   "maxCatCount"
 
#define CC_NUM_FEATURES   "numFeat"
 
#define CC_RECT   "rect"
 
#define CC_RECTS   "rects"
 
#define CC_TILTED   "tilted"
 
#define CV_HAAR_FEATURE_MAX   3
 
#define CV_SUM_OFFSETS(p0, p1, p2, p3, rect, step)
 
#define CV_TILTED_OFFSETS(p0, p1, p2, p3, rect, step)
 
#define FEATURES   "features"
 
#define HFP_NAME   "haarFeatureParams"
 
#define HOGF_NAME   "HOGFeatureParams"
 
#define LBPF_NAME   "lbpFeatureParams"
 
#define N_BINS   9
 
#define N_CELLS   4
 

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...
 

Enumerations

enum  {
  cv::TrackerSamplerCSC::MODE_INIT_POS = 1,
  cv::TrackerSamplerCSC::MODE_INIT_NEG = 2,
  cv::TrackerSamplerCSC::MODE_TRACK_POS = 3,
  cv::TrackerSamplerCSC::MODE_TRACK_NEG = 4,
  cv::TrackerSamplerCSC::MODE_DETECT = 5
}
 
enum  {
  cv::TrackerSamplerCS::MODE_POSITIVE = 1,
  cv::TrackerSamplerCS::MODE_NEGATIVE = 2,
  cv::TrackerSamplerCS::MODE_CLASSIFY = 3
}
 
enum  cv::CvFeatureParams::FeatureType {
  cv::CvFeatureParams::HAAR = 0,
  cv::CvFeatureParams::LBP = 1,
  cv::CvFeatureParams::HOG = 2
}
 
enum  cv::TrackerKCF::MODE {
  cv::TrackerKCF::GRAY = (1 << 0),
  cv::TrackerKCF::CN = (1 << 1),
  cv::TrackerKCF::CUSTOM = (1 << 2)
}
 Feature type to be used in the tracking grayscale, colornames, compressed color-names The modes available now: More...
 

Functions

template<class Feature >
void cv::_writeFeatures (const std::vector< Feature > features, FileStorage &fs, const Mat &featureMap)
 
float cv::CvHOGEvaluator::Feature::calc (const std::vector< Mat > &_hists, const Mat &_normSum, size_t y, int featComponent) const
 
uchar cv::CvLBPEvaluator::Feature::calc (const Mat &_sum, size_t y) const
 
float cv::calcNormFactor (const Mat &sum, const Mat &sqSum)
 
virtual float cv::CvHOGEvaluator::operator() (int varIdx, int sampleIdx) CV_OVERRIDE
 

Detailed Description

Long-term optical tracking API

Long-term optical tracking is an important issue for many computer vision applications in real world scenario. The development in this area is very fragmented and this API is an unique interface useful for plug several algorithms and compare them. This work is partially based on [180] and [122] .

These algorithms start from a bounding box of the target and with their internal representation they avoid the drift during the tracking. These long-term trackers are able to evaluate online the quality of the location of the target in the new frame, without ground truth.

There are three main components: the TrackerSampler, the TrackerFeatureSet and the TrackerModel. The first component is the object that computes the patches over the frame based on the last target location. The TrackerFeatureSet is the class that manages the Features, is possible plug many kind of these (HAAR, HOG, LBP, Feature2D, etc). The last component is the internal representation of the target, it is the appearence model. It stores all state candidates and compute the trajectory (the most likely target states). The class TrackerTargetState represents a possible state of the target. The TrackerSampler and the TrackerFeatureSet are the visual representation of the target, instead the TrackerModel is the statistical model.

A recent benchmark between these algorithms can be found in [235]

Creating Your Own Tracker

If you want to create a new tracker, here's what you have to do. First, decide on the name of the class for the tracker (to meet the existing style, we suggest something with prefix "tracker", e.g. trackerMIL, trackerBoosting) – we shall refer to this choice as to "classname" in subsequent.

Every tracker has three component TrackerSampler, TrackerFeatureSet and TrackerModel. The first two are instantiated from Tracker base class, instead the last component is abstract, so you must implement your TrackerModel.

TrackerSampler

TrackerSampler is already instantiated, but you should define the sampling algorithm and add the classes (or single class) to TrackerSampler. You can choose one of the ready implementation as TrackerSamplerCSC or you can implement your sampling method, in this case the class must inherit TrackerSamplerAlgorithm. Fill the samplingImpl method that writes the result in "sample" output argument.

Example of creating specialized TrackerSamplerAlgorithm TrackerSamplerCSC : :

class CV_EXPORTS_W TrackerSamplerCSC : public TrackerSamplerAlgorithm
{
public:
TrackerSamplerCSC( const TrackerSamplerCSC::Params &parameters = TrackerSamplerCSC::Params() );
~TrackerSamplerCSC();
...
protected:
bool samplingImpl( const Mat& image, Rect boundingBox, std::vector<Mat>& sample );
...
};

Example of adding TrackerSamplerAlgorithm to TrackerSampler : :

//sampler is the TrackerSampler
Ptr<TrackerSamplerAlgorithm> CSCSampler = new TrackerSamplerCSC( CSCparameters );
if( !sampler->addTrackerSamplerAlgorithm( CSCSampler ) )
return false;
//or add CSC sampler with default parameters
//sampler->addTrackerSamplerAlgorithm( "CSC" );
See also
TrackerSamplerCSC, TrackerSamplerAlgorithm

TrackerFeatureSet

TrackerFeatureSet is already instantiated (as first) , but you should define what kinds of features you'll use in your tracker. You can use multiple feature types, so you can add a ready implementation as TrackerFeatureHAAR in your TrackerFeatureSet or develop your own implementation. In this case, in the computeImpl method put the code that extract the features and in the selection method optionally put the code for the refinement and selection of the features.

Example of creating specialized TrackerFeature TrackerFeatureHAAR : :

class CV_EXPORTS_W TrackerFeatureHAAR : public TrackerFeature
{
public:
TrackerFeatureHAAR( const TrackerFeatureHAAR::Params &parameters = TrackerFeatureHAAR::Params() );
~TrackerFeatureHAAR();
void selection( Mat& response, int npoints );
...
protected:
bool computeImpl( const std::vector<Mat>& images, Mat& response );
...
};

Example of adding TrackerFeature to TrackerFeatureSet : :

//featureSet is the TrackerFeatureSet
Ptr<TrackerFeature> trackerFeature = new TrackerFeatureHAAR( HAARparameters );
featureSet->addTrackerFeature( trackerFeature );
See also
TrackerFeatureHAAR, TrackerFeatureSet

TrackerModel

TrackerModel is abstract, so in your implementation you must develop your TrackerModel that inherit from TrackerModel. Fill the method for the estimation of the state "modelEstimationImpl", that estimates the most likely target location, see [180] table I (ME) for further information. Fill "modelUpdateImpl" in order to update the model, see [180] table I (MU). In this class you can use the :cConfidenceMap and :cTrajectory to storing the model. The first represents the model on the all possible candidate states and the second represents the list of all estimated states.

Example of creating specialized TrackerModel TrackerMILModel : :

class TrackerMILModel : public TrackerModel
{
public:
TrackerMILModel( const Rect& boundingBox );
~TrackerMILModel();
...
protected:
void modelEstimationImpl( const std::vector<Mat>& responses );
void modelUpdateImpl();
...
};

And add it in your Tracker : :

bool TrackerMIL::initImpl( const Mat& image, const Rect2d& boundingBox )
{
...
//model is the general TrackerModel field of the general Tracker
model = new TrackerMILModel( boundingBox );
...
}

In the last step you should define the TrackerStateEstimator based on your implementation or you can use one of ready class as TrackerStateEstimatorMILBoosting. It represent the statistical part of the model that estimates the most likely target state.

Example of creating specialized TrackerStateEstimator TrackerStateEstimatorMILBoosting : :

class CV_EXPORTS_W TrackerStateEstimatorMILBoosting : public TrackerStateEstimator
{
class TrackerMILTargetState : public TrackerTargetState
{
...
};
public:
TrackerStateEstimatorMILBoosting( int nFeatures = 250 );
~TrackerStateEstimatorMILBoosting();
...
protected:
Ptr<TrackerTargetState> estimateImpl( const std::vector<ConfidenceMap>& confidenceMaps );
void updateImpl( std::vector<ConfidenceMap>& confidenceMaps );
...
};

And add it in your TrackerModel : :

//model is the TrackerModel of your Tracker
Ptr<TrackerStateEstimatorMILBoosting> stateEstimator = new TrackerStateEstimatorMILBoosting( params.featureSetNumFeatures );
model->setTrackerStateEstimator( stateEstimator );
See also
TrackerModel, TrackerStateEstimatorMILBoosting, TrackerTargetState

During this step, you should define your TrackerTargetState based on your implementation. TrackerTargetState base class has only the bounding box (upper-left position, width and height), you can enrich it adding scale factor, target rotation, etc.

Example of creating specialized TrackerTargetState TrackerMILTargetState : :

class TrackerMILTargetState : public TrackerTargetState
{
public:
TrackerMILTargetState( const Point2f& position, int targetWidth, int targetHeight, bool foreground, const Mat& features );
~TrackerMILTargetState();
...
private:
bool isTarget;
Mat targetFeatures;
...
};

Macro Definition Documentation

#define CC_FEATURE_PARAMS   "featureParams"
#define CC_FEATURE_SIZE   "featSize"
#define CC_FEATURES   FEATURES
#define CC_ISINTEGRAL   "isIntegral"
#define CC_MAX_CAT_COUNT   "maxCatCount"
#define CC_NUM_FEATURES   "numFeat"
#define CC_RECT   "rect"
#define CC_RECTS   "rects"
#define CC_TILTED   "tilted"
#define CV_HAAR_FEATURE_MAX   3
#define CV_SUM_OFFSETS (   p0,
  p1,
  p2,
  p3,
  rect,
  step 
)

#include <opencv2/tracking/feature.hpp>

Value:
/* (x, y) */ \
(p0) = (rect).x + (step) * (rect).y; \
/* (x + w, y) */ \
(p1) = (rect).x + (rect).width + (step) * (rect).y; \
/* (x + w, y) */ \
(p2) = (rect).x + (step) * ((rect).y + (rect).height); \
/* (x + w, y + h) */ \
(p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);
#define CV_TILTED_OFFSETS (   p0,
  p1,
  p2,
  p3,
  rect,
  step 
)

#include <opencv2/tracking/feature.hpp>

Value:
/* (x, y) */ \
(p0) = (rect).x + (step) * (rect).y; \
/* (x - h, y + h) */ \
(p1) = (rect).x - (rect).height + (step) * ((rect).y + (rect).height);\
/* (x + w, y + w) */ \
(p2) = (rect).x + (rect).width + (step) * ((rect).y + (rect).width); \
/* (x + w - h, y + w + h) */ \
(p3) = (rect).x + (rect).width - (rect).height \
+ (step) * ((rect).y + (rect).width + (rect).height);
#define FEATURES   "features"
#define HFP_NAME   "haarFeatureParams"
#define HOGF_NAME   "HOGFeatureParams"
#define LBPF_NAME   "lbpFeatureParams"
#define N_BINS   9
#define N_CELLS   4

Typedef Documentation

typedef std::vector<std::pair<Ptr<TrackerTargetState>, float> > cv::ConfidenceMap

#include <opencv2/tracking/tracker.hpp>

Represents the model of the target at frame \(k\) (all states and scores)

See [180] The set of the pair \(\langle \hat{x}^{i}_{k}, C^{i}_{k} \rangle\)

See also
TrackerTargetState
typedef std::vector<Ptr<TrackerTargetState> > cv::Trajectory

#include <opencv2/tracking/tracker.hpp>

Represents the estimate states for all frames.

[180] \(x_{k}\) is the trajectory of the target up to time \(k\)

See also
TrackerTargetState

Enumeration Type Documentation

anonymous enum
Enumerator
MODE_INIT_POS 

mode for init positive samples

MODE_INIT_NEG 

mode for init negative samples

MODE_TRACK_POS 

mode for update positive samples

MODE_TRACK_NEG 

mode for update negative samples

MODE_DETECT 

mode for detect samples

anonymous enum
Enumerator
MODE_POSITIVE 

mode for positive samples

MODE_NEGATIVE 

mode for negative samples

MODE_CLASSIFY 

mode for classify samples

Enumerator
HAAR 
LBP 
HOG 

Feature type to be used in the tracking grayscale, colornames, compressed color-names The modes available now:

  • "GRAY" – Use grayscale values as the feature
  • "CN" – Color-names feature
Enumerator
GRAY 
CN 
CUSTOM 

Function Documentation

template<class Feature >
void cv::_writeFeatures ( const std::vector< Feature >  features,
FileStorage fs,
const Mat featureMap 
)
float cv::CvHOGEvaluator::Feature::calc ( const std::vector< Mat > &  _hists,
const Mat _normSum,
size_t  y,
int  featComponent 
) const
inline
uchar cv::CvLBPEvaluator::Feature::calc ( const Mat _sum,
size_t  y 
) const
inline
float cv::calcNormFactor ( const Mat sum,
const Mat sqSum 
)
float cv::CvHOGEvaluator::operator() ( int  varIdx,
int  sampleIdx 
)
inlinevirtual