|Institution:||Curtin University of Technology|
|Department:||Electrical Engineering and Computing, Department of Computing|
|Full text PDF:||http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=194376&local_base=gen01-era02|
This thesis describes a bottom-up approach to estimating human pose over time based on monocular views with no restriction on human activities,Three approaches are proposed to address the weaknesses of existing approaches, including building a specific appearance model using clustering,utilising both the generic and specific appearance models in the estimation, and building an uncontaminated appearance model by removing backgroundpixels from the training samples. Experimental results show that the proposed system outperforms existing system significantly.