Tracking API implementation details

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 [233] and [164] .

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 TrackerContribSampler, the TrackerContribFeatureSet and the TrackerModel. The first component is the object that computes the patches over the frame based on the last target location. The TrackerContribFeatureSet 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 appearance 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 TrackerContribSampler and the TrackerContribFeatureSet are the visual representation of the target, instead the TrackerModel is the statistical model.

A recent benchmark between these algorithms can be found in [303]

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.

• Declare your tracker in modules/tracking/include/opencv2/tracking/tracker.hpp. Your tracker should inherit from Tracker (please, see the example below). You should declare the specialized Param structure, where you probably will want to put the data, needed to initialize your tracker. You should get something similar to :

  class CV_EXPORTS_W TrackerMIL : public Tracker
  {
   public:
    struct CV_EXPORTS Params
    {
      Params();
      //parameters for sampler
      float samplerInitInRadius;  // radius for gathering positive instances during init
      int samplerInitMaxNegNum;  // # negative samples to use during init
      float samplerSearchWinSize;  // size of search window
      float samplerTrackInRadius;  // radius for gathering positive instances during tracking
      int samplerTrackMaxPosNum;  // # positive samples to use during tracking
      int samplerTrackMaxNegNum;  // # negative samples to use during tracking
      int featureSetNumFeatures;  // #features
   
      void read( const FileNode& fn );
      void write( FileStorage& fs ) const;
    };

  of course, you can also add any additional methods of your choice. It should be pointed out, however, that it is not expected to have a constructor declared, as creation should be done via the corresponding create() method.
• Finally, you should implement the function with signature :

  Ptr<classname> classname::create(const classname::Params &parameters){
      ...
  }

  That function can (and probably will) return a pointer to some derived class of "classname", which will probably have a real constructor.

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

TrackerContribSampler

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

Example of creating specialized TrackerContribSamplerAlgorithm TrackerContribSamplerCSC : :

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

Example of adding TrackerContribSamplerAlgorithm to TrackerContribSampler : :

//sampler is the TrackerContribSampler
Ptr<TrackerContribSamplerAlgorithm> CSCSampler = new TrackerContribSamplerCSC( CSCparameters );
if( !sampler->addTrackerSamplerAlgorithm( CSCSampler ) )
 return false;
 
//or add CSC sampler with default parameters
//sampler->addTrackerSamplerAlgorithm( "CSC" );

See also

TrackerContribSamplerCSC, TrackerContribSamplerAlgorithm

TrackerContribFeatureSet

TrackerContribFeatureSet 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 TrackerContribFeatureHAAR in your TrackerContribFeatureSet 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 TrackerContribFeatureHAAR : :

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

Example of adding TrackerFeature to TrackerContribFeatureSet : :

//featureSet is the TrackerContribFeatureSet
Ptr<TrackerFeature> trackerFeature = new TrackerContribFeatureHAAR( HAARparameters );
featureSet->addTrackerFeature( trackerFeature );

See also

TrackerContribFeatureHAAR, TrackerContribFeatureSet

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 [233] table I (ME) for further information. Fill "modelUpdateImpl" in order to update the model, see [233] 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;
  ...
 
};

Namespaces
namespace	cv::detail::tracking::contrib_feature
namespace	cv::detail::tracking::kalman_filters
namespace	cv::detail::tracking::online_boosting
namespace	cv::detail::tracking::tbm
namespace	cv::detail::tracking::tld

Classes
class	cv::detail::tracking::TrackerContribFeature
	Abstract base class for TrackerContribFeature that represents the feature. More...
class	cv::detail::tracking::TrackerContribFeatureHAAR
	TrackerContribFeature based on HAAR features, used by TrackerMIL and many others algorithms. More...
class	cv::detail::tracking::TrackerContribFeatureSet
	Class that manages the extraction and selection of features. More...
class	cv::detail::tracking::TrackerContribSampler
	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::detail::tracking::TrackerContribSamplerAlgorithm
	Abstract base class for TrackerContribSamplerAlgorithm that represents the algorithm for the specific sampler. More...
class	cv::detail::tracking::TrackerContribSamplerCSC
	TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL. More...
class	cv::detail::tracking::TrackerFeature
	Abstract base class for TrackerFeature that represents the feature. More...
class	cv::detail::tracking::TrackerFeatureFeature2d
	TrackerContribFeature based on Feature2D. More...
class	cv::detail::tracking::TrackerFeatureHOG
	TrackerContribFeature based on HOG. More...
class	cv::detail::tracking::TrackerFeatureLBP
	TrackerContribFeature based on LBP. More...
class	cv::detail::tracking::TrackerFeatureSet
	Class that manages the extraction and selection of features. More...
class	cv::detail::tracking::TrackerModel
	Abstract class that represents the model of the target. More...
class	cv::detail::tracking::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::detail::tracking::TrackerSamplerAlgorithm
	Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler. More...
class	cv::detail::tracking::TrackerSamplerCS
	TrackerContribSampler based on CS (current state), used by algorithm TrackerBoosting. More...
class	cv::detail::tracking::TrackerSamplerCSC
	TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL. More...
class	cv::detail::tracking::TrackerSamplerPF
	This sampler is based on particle filtering. More...
class	cv::detail::tracking::TrackerStateEstimator
	Abstract base class for TrackerStateEstimator that estimates the most likely target state. More...
class	cv::detail::tracking::TrackerStateEstimatorAdaBoosting
	TrackerStateEstimatorAdaBoosting based on ADA-Boosting. More...
class	cv::detail::tracking::TrackerStateEstimatorSVM
	TrackerStateEstimator based on SVM. More...
class	cv::detail::tracking::TrackerTargetState
	Abstract base class for TrackerTargetState that represents a possible state of the target. More...

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

Functions
void	cv::detail::tracking::computeInteractionMatrix (const cv::Mat &uv, const cv::Mat &depths, const cv::Mat &K, cv::Mat &J)
	Compute the interaction matrix ( [132] [52] [53] ) for a set of 2D pixels. This is usually used in visual servoing applications to command a robot to move at desired pixel locations/velocities. By inverting this matrix, one can estimate camera spatial velocity i.e., the twist.
cv::Vec6d	cv::detail::tracking::computeTwist (const cv::Mat &uv, const cv::Mat &duv, const cv::Mat &depths, const cv::Mat &K)
	Compute the camera twist from a set of 2D pixel locations, their velocities, depth values and intrinsic parameters of the camera. The pixel velocities are usually obtained from optical flow algorithms, both dense and sparse flow can be used to compute the flow between images and duv computed by dividing the flow by the time interval between the images.

Typedef Documentation

ConfidenceMap

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

#include <opencv2/video/detail/tracking.detail.hpp>

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

See [233] The set of the pair \(\langle \hat{x}^{i}_{k}, C^{i}_{k} \rangle\)

See also

TrackerTargetState

Trajectory

typedef std::vector<Ptr<TrackerTargetState> > cv::detail::tracking::Trajectory

#include <opencv2/video/detail/tracking.detail.hpp>

Represents the estimate states for all frames.

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

See also

TrackerTargetState

Function Documentation

computeInteractionMatrix()

void cv::detail::tracking::computeInteractionMatrix	(	const cv::Mat &	uv,
		const cv::Mat &	depths,
		const cv::Mat &	K,
		cv::Mat &	J )

#include <opencv2/tracking/twist.hpp>

Compute the interaction matrix ( [132] [52] [53] ) for a set of 2D pixels. This is usually used in visual servoing applications to command a robot to move at desired pixel locations/velocities. By inverting this matrix, one can estimate camera spatial velocity i.e., the twist.

Parameters

uv	2xN matrix of 2D pixel locations
depths	1xN matrix of depth values
K	3x3 camera intrinsic matrix
J	2Nx6 interaction matrix

computeTwist()

cv::Vec6d cv::detail::tracking::computeTwist	(	const cv::Mat &	uv,
		const cv::Mat &	duv,
		const cv::Mat &	depths,
		const cv::Mat &	K )

#include <opencv2/tracking/twist.hpp>

Compute the camera twist from a set of 2D pixel locations, their velocities, depth values and intrinsic parameters of the camera. The pixel velocities are usually obtained from optical flow algorithms, both dense and sparse flow can be used to compute the flow between images and duv computed by dividing the flow by the time interval between the images.

Parameters

uv	2xN matrix of 2D pixel locations
duv	2Nx1 matrix of 2D pixel velocities
depths	1xN matrix of depth values
K	3x3 camera intrinsic matrix

Returns

cv::Vec6d 6x1 camera twist