A Hierarchical Model for Continuous Gesture Recognition Using Kinect

Søren Kejser Jensen, Christoffer Moesgaard, Christoffer Samuel Nielsen, Sine Lyhne Viesmose

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Human gesture recognition is an area, which has been studied thoroughly in recent years,and close to100% recognition rates in restricted environments have been achieved, often either with single separated gestures in the input stream, or with computationally intensive systems. The results are unfortunately not as striking, when it comes to a continuous stream of gestures. In this paper we introduce a hierarchical system for gesture recognition for use in a gaming setting, with a continuous stream of data. Layer 1 is based on Nearest Neighbor Search and layer 2 uses Hidden Markov Models. The system uses features that are computed from Microsoft Kinect skeletons. We propose a new set of features, the relative angles of the limbs from Kinect's axes to use in NNS. The new features show a 10 percent point increase in precision when compared with features from previously published results. We also propose a way of attributing recognised gestures with a force attribute, for use in gaming. The recognition rate in layer 1 is 68.2%, with an even higher rate for simple gestures. Layer 2 reduces the noise and has aaverage recognition rate of 85.1%. When some simple constraints are added we reach a precision of 90.5% with a recall of 91.4%.
TitelTwelfth Scandinavian Conference on Artificial Intelligence
RedaktørerManfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani
ForlagIOS Press
ISBN (Trykt)978-1-61499-329-2
ISBN (Elektronisk)978-1-61499-330-8
StatusUdgivet - 2013
Begivenhed12th Scandinavian Conference on Artificial Intelligence - Aalborg, Danmark
Varighed: 20 nov. 201322 nov. 2013
Konferencens nummer: 12


Konference12th Scandinavian Conference on Artificial Intelligence
NavnFrontiers in Artificial Intelligence and Applications