KEYWORDS: Detection and tracking algorithms, Feature extraction, Head, Cameras, Data modeling, Image segmentation, 3D modeling, Data conversion, RGB color model, Gesture recognition
A multi-class human posture detection and recognition algorithm using Kinect based geometric features is
presented. The three dimensional skeletal data from the Kinect is converted to a set of angular features. The postures are
classified using a support vector machines classifier with polynomial kernel. Detection of posture is done by
thresholding the posture probability. The algorithm provided a recognition accuracy of 95.78% when tested using a 10
class dataset containing 6000 posture samples. The precision and recall rates of the detection system are 100% and
98.54% respectively.
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