DocumentCode :
3011758
Title :
Human activity recognition via motion and vision data fusion
Author :
Zhu, Chun ; Cheng, Qi ; Sheng, Weihua
Author_Institution :
Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA
fYear :
2010
fDate :
7-10 Nov. 2010
Firstpage :
332
Lastpage :
336
Abstract :
Automated recognition of human daily activities is very important for human-robot interaction (HRI) in assisted living systems. We propose a Bayesian framework to integrate motion sensor observations and the location information from a vision system for human daily activity recognition. Two problems are studied in this paper: enhancing activity recognition through the fusion of two channels of information and learning the environment through the activity distribution map. The entropy associated with human activity recognition is adopted as an evaluation metric in both problems. The simulation results demonstrate the feasibility of the proposed methods.
Keywords :
Bayes methods; computer vision; entropy; human-robot interaction; image motion analysis; image recognition; sensor fusion; Bayesian framework; activity distribution map; activity recognition enhancement; assisted living systems; automated recognition; entropy; human daily activity recognition; human-robot interaction; integrate motion sensor observations; vision data fusion; Accuracy; Entropy; Feature extraction; Humans; Layout; Optical sensors; Three dimensional displays; Activity recognition; Bayesian framework; wearable computing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
ISSN :
1058-6393
Print_ISBN :
978-1-4244-9722-5
Type :
conf
DOI :
10.1109/ACSSC.2010.5757529
Filename :
5757529
Link To Document :
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