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GestureRecognitionToolkit
Version: 0.2.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
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GestureRecognitionToolkit
Version: 0.2.0
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, c++ machine learning library for real-time gesture recognition.
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This class implements the Adaptive Naive Bayes Classifier algorithm. The Adaptive Naive Bayes Classifier (ANBC) is a naive but powerful classifier that works very well on both basic and more complex recognition problems. More...
Go to the source code of this file.
Classes | |
| class | ANBC |
Macros | |
| #define | MIN_SCALE_VALUE 1.0e-10 |
| #define | MAX_SCALE_VALUE 1 |
This class implements the Adaptive Naive Bayes Classifier algorithm. The Adaptive Naive Bayes Classifier (ANBC) is a naive but powerful classifier that works very well on both basic and more complex recognition problems.
The ANBC algorithm is a supervised learning algorithm that can be used to classify any type of N-dimensional signal. The ANBC algorithm essentially works by fitting an N-dimensional Gaussian distribution to each class (i.e. gesture) during the training phase. New gestures can then be recognized in the prediction phase by finding the gesture that results in the maximum likelihood value (given the new sensor data and each of the Gaussian distributions). The ANBC algorithm also computes rejection thresholds that enable the algorithm to automatically reject sensor values that are not the K gestures the algorithm has been trained to recognized (without being explicitly told during the prediction phase if a gesture is, or is not, being performed).
In addition, the ANBC algorithm enables you to weight the importance of each dimension for each gesture. For instance, imagine that you want to create a recognition system that can recognize a user's left-handed gestures, right-handed gestures, and two-handed gestures. To track the user's movements you use a depth sensor and skeleton-tracking algorithm that can track any user who stands in front of the depth sensor and sends out the x-y-z joint position of the user's two hands (along with the user's other joints) at 30Hz. You use the 3-dimensional joint data for each hand to create a 6-dimensional vector (containing {leftHandX, leftHandY, leftHandZ, rightHandX, rightHandY, rightHandZ}) as input to the ANBC algorithm. The ANBC algorithm enables you to weight each dimension of this vector for each of the 3 types of gestures you want to recognize (i.e. left-handed, right-handed, and two-handed gestures), so for a left-handed gesture you would set the weights for this class to {1,1,1,0,0,0}, for the right-handed gesture you would set the weights for this class to {0,0,0,1,1,1}, and for the two-handed gesture you would set the weights for this class to {1,1,1,1,1,1}. You only need to set these weights values once, before you train the ANBC model, the weights will then automatically be incorporated into the Gaussian models for each gesture (and therefore nothing special needs to be done for the prediction phase). You can set the weights using the setWeights(LabelledClassificationData weightsData) function.
The ANBC algorithm is part of the GRT classification modules.
Definition in file ANBC.h.
| #define MIN_SCALE_VALUE 1.0e-10 |
GRT MIT License Copyright (c) <2012> <Nicholas Gillian, Media Lab, MIT>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
1.8.11 
