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Human activity recognition by Gait Analysis
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Date
2011
Author
Kepenekci, Burcu
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This thesis analyzes the human action recognition problem. Human actions are modeled as a time evolving temporal texture. Gabor filters, which are proved to be a robust 2D texture representation tool by detecting spatial points with high variation, is extended to 3D domain to capture motion texture features. A well known filtering algorithm and a recent unsupervised clustering algorithm, the Genetic Chromodynamics, are combined to select salient spatio-temporal features of the temporal texture and to segment the activity sequence into temporal texture primitives. Each activity sequence is represented as a composition of temporal texture primitives with its salient spatio-temporal features, which are also the symbols of our codebook. To overcome temporal variation between different performances of the same action, a Profile Hidden Markov Model is applied with Viterbi Path Counting (ensemble training). Not only parameters and structure but also codebook is learned during training.
Subject Keywords
Optical pattern recognition.
,
Human mechanics
URI
http://etd.lib.metu.edu.tr/upload/12613089/index.pdf
https://hdl.handle.net/11511/20607
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Graduate School of Natural and Applied Sciences, Thesis
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B. Kepenekci, “Human activity recognition by Gait Analysis,” Ph.D. - Doctoral Program, Middle East Technical University, 2011.