Fall detection based on skeleton extraction

Authors:
Zhen-Peng Bian;Lap-Pui Chau;Nadia Magnenat-Thalmann
Affiliations:
Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Nanyang Technological University, Singapore
Venue:
Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
Year:
2012

Citing 4
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Abstract

This paper presents an improved skeleton extraction from depth video for fall detection based on fast randomized decision forest (RDF) algorithm. Due to the human's body orientation changes dramatically during falling, it reduces the accuracy of tracking. The human's orientation needs to be corrected before the process by RDF. A rotation to correct the orientation is required frame by frame. Experimental results show that with the help of correction our proposed fall detection method could outperform the existing RDF based method.