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.
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.
Discriminative Correlation Filter Tracker with Channel and Spatial Reliability
GOTURN 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.
KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed. The original paper of KCF is available at http://home.isr.uc.pt/~henriques/circulant/index.html as well as the matlab implementation.
Median Flow tracker implementation. The tracker is suitable for very smooth and predictable movements when object is visible throughout the whole sequence.It's quite and accurate for this type of problems (in particular, it was shown by authors to outperform MIL). During the implementation period the code at http://www.aonsquared.co.uk/node/5, the courtesy of the author Arthur Amarra, was used for the reference purpose.
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. Original code can be found here http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml
MOSSE Visual Object Tracking using Adaptive Correlation Filters
TLD is a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection.
Feature type to be used in the tracking grayscale, colornames, compressed color-names The modes available now: - "GRAY" -- Use grayscale values as the feature - "CN" -- Color-names feature