Authors:
Amin Dadgar
and
Guido Brunnett
Affiliation:
Computer Science, Chemnitz University of Technology, Straße der Nationen 62, 09111, Chemnitz, Germany
Keyword(s):
Generative Poselet, Topolet, Topological-based Temporal Model, Hidden Markov Model, Gestures’ Comprehensive Set, Context-Free Gesture Recognition, Description-based (Data) Specification.
Abstract:
We propose a type of time-series model for hierarchical hand posture database which can be viewed as a Markovian temporal structure. The model employs the topology of the points’ cloud, existing in each layer of the database, and exploits a novel type of atomic structure, we refer to as Topolet. Moreover, our temporal structure utilizes a modified version of another atomic gesture structure, known as Poselet. That modification considers Poselets from the vector-based generative perspective (instead of the pixel-based discriminative one). The results suggest a considerable improvement in the accuracy and time-complexity. Furthermore, in contrast to other approaches, our Topolet is capable of considering random gestures, thus introduces a comprehensive set of gestures (suitable for context-free application domain) within the shape-based approach. We prove that the Topolet could be enhanced to different resolutions of gestures’ set which provide the system with the potential to be adapte
d to different application requirements.
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