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#Gesture Recognition Toolkit

Welcome to the wiki page for the Gesture Recognition Toolkit.

This wiki is in the process of being migrated to github, the original version of the wiki can be found here.

The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library that has been specifically designed for real-time gesture recognition.

In addition to a comprehensive C++ API, the GRT now also includes an easy-to-use graphical user interface:

You can find out more about the GUI here.

##Algorithms

###Classifiers

• Adaboost
• Adaptive Naive Bayes Classifier (ANBC)
• Bootstrap Aggregator (BAG)
• Decision Tree
• Dynamic Time Warping (DTW)
• Gaussian Mixture Models (GMM)
• Hidden Markov Models (HMM Discrete)
• Hidden Markov Models (HMM Continuous)
• K-Nearest Neighbors (KNN)
• MinDist
• RandomForests
• Support Vector Machine (SVM)
• Softmax

###Regressifiers

• LinearRegression
• LogisticRegression
• MLP
• MultidimensionalRegression

##Reference You can find a full list of the main classes in the GRT on the references page.

This provides a higher level description of the core classes in the GRT, including an overview of what the classes do, how they do it, the main advantages and disadvantages of using that class over a similar class, and some example code to demonstrate how to use the class.