cv::detail::tracking Namespace Reference

Namespaces
namespace	contrib_feature
namespace	kalman_filters
namespace	online_boosting
namespace	tbm
namespace	tld

Classes
class	AugmentedUnscentedKalmanFilterParams
	Augmented Unscented Kalman filter parameters. The class for initialization parameters of Augmented Unscented Kalman filter. More...
class	BaseClassifier
class	ClassifierThreshold
class	CvFeatureEvaluator
class	CvFeatureParams
class	CvHaarEvaluator
class	CvHaarFeatureParams
class	CvHOGEvaluator
struct	CvHOGFeatureParams
class	CvLBPEvaluator
struct	CvLBPFeatureParams
class	CvParams
class	Detector
class	EstimatedGaussDistribution
class	StrongClassifierDirectSelection
class	TrackerContribFeature
	Abstract base class for TrackerContribFeature that represents the feature. More...
class	TrackerContribFeatureHAAR
	TrackerContribFeature based on HAAR features, used by TrackerMIL and many others algorithms. More...
class	TrackerContribFeatureSet
	Class that manages the extraction and selection of features. More...
class	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	TrackerContribSamplerAlgorithm
	Abstract base class for TrackerContribSamplerAlgorithm that represents the algorithm for the specific sampler. More...
class	TrackerContribSamplerCSC
	TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL. More...
class	TrackerFeature
	Abstract base class for TrackerFeature that represents the feature. More...
class	TrackerFeatureFeature2d
	TrackerContribFeature based on Feature2D. More...
class	TrackerFeatureHOG
	TrackerContribFeature based on HOG. More...
class	TrackerFeatureLBP
	TrackerContribFeature based on LBP. More...
class	TrackerFeatureSet
	Class that manages the extraction and selection of features. More...
class	TrackerModel
	Abstract class that represents the model of the target. More...
class	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	TrackerSamplerAlgorithm
	Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler. More...
class	TrackerSamplerCS
	TrackerContribSampler based on CS (current state), used by algorithm TrackerBoosting. More...
class	TrackerSamplerCSC
	TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL. More...
class	TrackerSamplerPF
	This sampler is based on particle filtering. More...
class	TrackerStateEstimator
	Abstract base class for TrackerStateEstimator that estimates the most likely target state. More...
class	TrackerStateEstimatorAdaBoosting
	TrackerStateEstimatorAdaBoosting based on ADA-Boosting. More...
class	TrackerStateEstimatorSVM
	TrackerStateEstimator based on SVM. More...
class	TrackerTargetState
	Abstract base class for TrackerTargetState that represents a possible state of the target. More...
class	UkfSystemModel
	Model of dynamical system for Unscented Kalman filter. The interface for dynamical system model. It contains functions for computing the next state and the measurement. It must be inherited for using UKF. More...
class	UnscentedKalmanFilter
	The interface for Unscented Kalman filter and Augmented Unscented Kalman filter. More...
class	UnscentedKalmanFilterParams
	Unscented Kalman filter parameters. The class for initialization parameters of Unscented Kalman filter. More...
class	WeakClassifierHaarFeature

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

Functions
template<class Feature >
void	_writeFeatures (const std::vector< Feature > features, FileStorage &fs, const Mat &featureMap)
float	calcNormFactor (const Mat &sum, const Mat &sqSum)
void	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	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.
Ptr< UnscentedKalmanFilter >	createAugmentedUnscentedKalmanFilter (const AugmentedUnscentedKalmanFilterParams &params)
	Augmented Unscented Kalman Filter factory method.
Ptr< UnscentedKalmanFilter >	createUnscentedKalmanFilter (const UnscentedKalmanFilterParams &params)
	Unscented Kalman Filter factory method.