Description:

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.

The GRT has been designed to:

(1) be easy to use and integrate into your existing c++ projects (2) be compatible with any type of sensor or data input (3) be easy to rapidly train with your own gestures (4) be easy to extend and adapt with your own custom processing or feature extraction algorithms (if needed).

The GRT features a large number of algorithms that can be used to:

(1) recognize static postures (such as if a user has their hands in a specific posture or if a device fitted with an accelerometer is being held in a distinct orientation) (2) recognize dynamic temporal gestures (such as a swipe or tap gesture) (3) perform regression (i.e. continually map an input signal to an output signal, such as mapping the angle of a user's hands to the angle a steering wheel should be turned in a driving game).

The GRT currently works across several operating systems including:

(1) Windows (Tested on Windows XP, Windows 7) (2) OS X (Tested on 10.7) (3) Linux (Tested on Ubuntu 12).

The current build of the GRT contains machine-learning algorithms such as:

(1) Adaptive Naive Bayes Classifier (2) K-Nearest Neighbor Classifier (3) AdaBoost (4) MinDist (5) Gaussian Mixture Model (3) Dynamic Time Warping (4) Support Vector Machine (a wrapper for libsvm) (5) Artificial Neural Network (Multi Layer Perceptron) (6) Logistic Regression.

In addition to the machine-learning algorithms, the GRT also contains a large number of pre-processing, post-processing, and feature-extraction algorithms such as:

(1) Low Pass Filter (2) High Pass Filter (3) Moving Average Filter (4) Derivative (5) Zero Crossing (6) FFT (7) KMeans Quantizer (8) Class Label Filter (9) Class Label Timeout Filter.

More information can be found on the main GRT wiki: http://www.nickgillian.com/wiki/pmwiki.php/GRT/GestureRecognitionToolkit

You can download the GRT via googlecode: https://code.google.com/p/gesture-recognition-toolkit/source/checkout

Changes to previous version:

Added logistic regression module.

BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Mac Os X

Data Formats: Plain Ascii, Csv

Tags: Classification, Clustering, Support Vector Machines, Dtw, Logistic Regression, Hidden Markov Model, Feature Extraction, Gesture Recognition

Archive: download here

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