Paper Info
Title
Day or Night Activity Recognition From Video Using Fuzzy Clustering Techniques
Abstract
We present an approach for activity state recognition implemented on data collected from various sensors—standard web cameras under normal illumination, web cameras using infrared lighting, and the inexpensive Microsoft Kinect camera system. Sensors such as the Kinect ensure that activity segmentation is possible during the daytime as well as night. This is especially useful for activity monitoring of older adults since falls are more prevalent at night than during the day. This paper is an application of fuzzy set techniques to a new domain. The approach described herein is capable of accurately detecting several different activity states related to fall detection and fall risk assessment including sitting, being upright, and being on the floor to ensure that elderly residents get the help they need quickly in case of emergencies and ultimately to help prevent such emergencies.
Year
DOI
Venue
2014
10.1109/TFUZZ.2013.2260756
IEEE Transactions on Fuzzy Systems
Keywords
Field
DocType
assisted living,cameras,fuzzy set theory,gesture recognition,image segmentation,pattern clustering,sensors,video signal processing,activity segmentation,activity state recognition,day activity recognition,elderly residents,fall detection assessment,fall risk assessment,fuzzy clustering techniques,fuzzy set techniques,inexpensive Microsoft Kinect camera system,infrared lighting,night activity recognition,older adult activity monitoring,sensors,web cameras,Activity labeling,depth images,fuzzy clustering,image moments,infrared images
Fuzzy clustering,Computer vision,State recognition,Activity recognition,Segmentation,Fall risk,Fuzzy set,Artificial intelligence,Sitting,Image moment,Mathematics
Journal
Volume
Issue
ISSN
22
3
1063-6706
Citations 
PageRank 
References 
6
0.50
10
Authors
4
Name
Order
Citations
PageRank
Tanvi Banerjee18316.41
James M. Keller23201436.69
Marjorie Skubic31045105.36
Erik E. Stone438131.42