OpenCV  4.10.0
Open Source Computer Vision
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Namespaces | Classes | Typedefs | Functions
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< UnscentedKalmanFiltercreateAugmentedUnscentedKalmanFilter (const AugmentedUnscentedKalmanFilterParams &params)
 Augmented Unscented Kalman Filter factory method.
 
Ptr< UnscentedKalmanFiltercreateUnscentedKalmanFilter (const UnscentedKalmanFilterParams &params)
 Unscented Kalman Filter factory method.