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class | cv::MultiTracker |
| This class is used to track multiple objects using the specified tracker algorithm. The MultiTracker is naive implementation of multiple object tracking. It process the tracked objects independently without any optimization accross the tracked objects. More...
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class | cv::MultiTracker_Alt |
| Base abstract class for the long-term Multi Object Trackers: More...
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class | cv::MultiTrackerTLD |
| Multi Object Tracker for TLD. TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. More...
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struct | cv::TrackerCSRT::Params |
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struct | cv::TrackerGOTURN::Params |
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struct | cv::TrackerKCF::Params |
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struct | cv::TrackerTLD::Params |
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struct | cv::TrackerMedianFlow::Params |
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struct | cv::TrackerBoosting::Params |
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struct | cv::TrackerMIL::Params |
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struct | cv::TrackerFeatureHAAR::Params |
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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...
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struct | cv::TrackerSamplerCS::Params |
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struct | cv::TrackerSamplerCSC::Params |
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class | cv::Tracker |
| Base abstract class for the long-term tracker: More...
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class | cv::TrackerStateEstimatorAdaBoosting::TrackerAdaBoostingTargetState |
| Implementation of the target state for TrackerAdaBoostingTargetState. More...
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class | cv::TrackerBoosting |
| This is a real-time object tracking based on a novel on-line version of the AdaBoost algorithm. More...
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class | cv::TrackerCSRT |
| Discriminative Correlation Filter Tracker with Channel and Spatial Reliability. More...
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class | cv::TrackerFeature |
| Abstract base class for TrackerFeature that represents the feature. More...
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class | cv::TrackerFeatureFeature2d |
| TrackerFeature based on Feature2D. More...
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class | cv::TrackerFeatureHAAR |
| TrackerFeature based on HAAR features, used by TrackerMIL and many others algorithms. More...
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class | cv::TrackerFeatureHOG |
| TrackerFeature based on HOG. More...
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class | cv::TrackerFeatureLBP |
| TrackerFeature based on LBP. More...
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class | cv::TrackerFeatureSet |
| Class that manages the extraction and selection of features. More...
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class | cv::TrackerGOTURN |
| GOTURN ([85]) is kind of trackers based on Convolutional Neural Networks (CNN). While taking all advantages of CNN trackers, GOTURN is much faster due to offline training without online fine-tuning nature. GOTURN tracker addresses the problem of single target tracking: given a bounding box label of an object in the first frame of the video, we track that object through the rest of the video. NOTE: Current method of GOTURN does not handle occlusions; however, it is fairly robust to viewpoint changes, lighting changes, and deformations. Inputs of GOTURN are two RGB patches representing Target and Search patches resized to 227x227. Outputs of GOTURN are predicted bounding box coordinates, relative to Search patch coordinate system, in format X1,Y1,X2,Y2. Original paper is here: http://davheld.github.io/GOTURN/GOTURN.pdf As long as original authors implementation: https://github.com/davheld/GOTURN#train-the-tracker Implementation of training algorithm is placed in separately here due to 3d-party dependencies: https://github.com/Auron-X/GOTURN_Training_Toolkit GOTURN architecture goturn.prototxt and trained model goturn.caffemodel are accessible on opencv_extra GitHub repository. More...
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class | cv::TrackerKCF |
| KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed. This tracking method is an implementation of [86] which is extended to KCF with color-names features ([41]). The original paper of KCF is available at http://www.robots.ox.ac.uk/~joao/publications/henriques_tpami2015.pdf as well as the matlab implementation. For more information about KCF with color-names features, please refer to http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html. More...
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class | cv::TrackerMedianFlow |
| Median Flow tracker implementation. More...
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class | cv::TrackerMIL |
| The MIL algorithm trains a classifier in an online manner to separate the object from the background. More...
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class | cv::TrackerStateEstimatorMILBoosting::TrackerMILTargetState |
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class | cv::TrackerModel |
| Abstract class that represents the model of the target. It must be instantiated by specialized tracker. More...
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class | cv::TrackerMOSSE |
| the MOSSE tracker note, that this tracker works with grayscale images, if passed bgr ones, they will get converted internally. [19] Visual Object Tracking using Adaptive Correlation Filters More...
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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...
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class | cv::TrackerSamplerAlgorithm |
| Abstract base class for TrackerSamplerAlgorithm that represents the algorithm for the specific sampler. More...
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class | cv::TrackerSamplerCS |
| TrackerSampler based on CS (current state), used by algorithm TrackerBoosting. More...
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class | cv::TrackerSamplerCSC |
| TrackerSampler based on CSC (current state centered), used by MIL algorithm TrackerMIL. More...
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class | cv::TrackerSamplerPF |
| This sampler is based on particle filtering. More...
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class | cv::TrackerStateEstimator |
| Abstract base class for TrackerStateEstimator that estimates the most likely target state. More...
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class | cv::TrackerStateEstimatorAdaBoosting |
| TrackerStateEstimatorAdaBoosting based on ADA-Boosting. More...
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class | cv::TrackerStateEstimatorMILBoosting |
| TrackerStateEstimator based on Boosting. More...
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class | cv::TrackerStateEstimatorSVM |
| TrackerStateEstimator based on SVM. More...
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class | cv::TrackerTargetState |
| Abstract base class for TrackerTargetState that represents a possible state of the target. More...
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class | cv::TrackerTLD |
| TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. More...
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