Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99395
Title: Fall detection based on skeleton extraction
Authors: Chau, Lap-Pui
Bian, Zhen-Peng
Magnenat-Thalmann, Nadia
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Bian, Z. P., Chau, L. P., & Magnenat-Thalmann, N. (2012). Fall detection based on skeleton extraction. Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry - VRCAI '12, 91-94.
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.
URI: https://hdl.handle.net/10356/99395
http://hdl.handle.net/10220/12824
DOI: 10.1145/2407516.2407544
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:IMI Conference Papers

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