Base abstract class for the long-term tracker:
class CV_EXPORTS_W Tracker : public virtual Algorithm
{
virtual ~Tracker();
bool init( const Mat& image, const Rect2d& boundingBox );
bool update( const Mat& image, Rect2d& boundingBox );
static Ptr<Tracker> create( const String& trackerType );
};
Initialize the tracker with a know bounding box that surrounding the target
| Parameters: |
|
|---|---|
| Returns: | True if initialization went succesfully, false otherwise |
Update the tracker, find the new most likely bounding box for the target
| Parameters: |
|
|---|---|
| Returns: | True means that target was located and false means that tracker cannot locate target in current frame. Note, that latter does not imply that tracker has failed, maybe target is indeed missing from the frame (say, out of sight) |
Creates a tracker by its name.
| Parameters: |
|
|---|
The following detector types are supported:
If you want 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. Also, you should decide upon the name of the tracker, is it will be known to user (the current style suggests using all capitals, say MIL or BOOSTING) – we’ll call it a “name”.
Declare your tracker in 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. Also don’t forget to put the BOILERPLATE_CODE(name,classname) macro inside the class declaration. That macro will generate static createTracker() function, which we’ll talk about later. 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 createTracker() method.
In src/tracker.cpp file add BOILERPLATE_CODE(name,classname) line to the body of Tracker::create() method you will find there, like
Ptr<Tracker> Tracker::create( const String& trackerType )
{
BOILERPLATE_CODE("BOOSTING",TrackerBoosting);
BOILERPLATE_CODE("MIL",TrackerMIL);
return Ptr<Tracker>();
}
Finally, you should implement the function with signature
Ptr<classname> classname::createTracker(const classname::Params ¶meters){
...
}
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 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 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 ¶meters = 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
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 ¶meters = 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
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 [AAM] table I (ME) for further information. Fill “modelUpdateImpl” in order to update the model, see [AAM] table I (MU). In this class you can use the ConfidenceMap and Trajectory 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 od 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 );
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;
...
};
To try your tracker you can use the demo at https://github.com/lenlen/opencv/blob/tracking_api/samples/cpp/tracker.cpp.
The first argument is the name of the tracker and the second is a video source.