Human activity recognition by Gait Analysis

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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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Citation Formats

B. Kepenekci, “Human activity recognition by Gait Analysis,” Ph.D. - Doctoral Program, Middle East Technical University, 2011.