OpenCvSharp.Tracking Namespace

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.

Base abstract class for the long-term tracker

This is a real-time object tracking based on a novel on-line version of the AdaBoost algorithm. The classifier uses the surrounding background as negative examples in update step to avoid the drifting problem.The implementation is based on @cite OLB.

the CSRT tracker The implementation is based on @cite Lukezic_IJCV2018 Discriminative Correlation Filter with Channel and Spatial Reliability

GOTURN (@cite GOTURN) is kind of trackers based on Convolutional Neural Networks (CNN).

KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed. * This tracking method is an implementation of @cite KCF_ECCV which is extended to KFC with color-names features(@cite KCF_CN). * 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].

Median Flow tracker implementation.

The MIL algorithm trains a classifier in an online manner to separate the object from the background. Multiple Instance Learning avoids the drift problem for a robust tracking.The implementation is based on @cite MIL. Original code can be found here [http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml]

MOSSE tracker. this tracker works with grayscale images, if passed bgr ones, they will get converted internally.

TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection. The tracker follows the object from frame to frame.The detector localizes all appearances that have been observed so far and corrects the tracker if necessary.The learning estimates detector’s errors and updates it to avoid these errors in the future.The implementation is based on @cite TLD . The Median Flow algorithm (see cv::TrackerMedianFlow) was chosen as a tracking component in this implementation, following authors. Tracker is supposed to be able to handle rapid motions, partial occlusions, object absence etc.

CSRT Params