Authors:
Amin Dadgar
and
Guido Brunnett
Affiliation:
Computer Science, Chemnitz University of Technology, Straße der Nationen 62, 09111, Chemnitz and Germany
Keyword(s):
Generative Poselet, Topolet, Topological-based Temporal Model, Hidden Markov Model, Gestures’ Comprehensive Set, Context-Free Gesture Recognition, Description-based (Data) Specification.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Features Extraction
;
Geometry and Modeling
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Segmentation and Grouping
;
Shape Representation and Matching
;
Software Engineering
;
Tracking and Visual Navigation
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 adapt
ed to different application requirements.
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