Similarity-based analysis for large networks of ultra-low resolution sensors

By analyzing the similarities between bit streams coming from a network of motion detectors, we can recover the network geometry and discover structure in the human behavior being observed. This means that a low-cost network of sensors can provide powerful contextual information to building systems:...

Full description

Saved in:
Bibliographic Details
Published in:Pattern recognition 2006-10, Vol.39 (10), p.1918-1931
Main Authors: Wren, Christopher R., Minnen, David C., Rao, Srinivasa G.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:By analyzing the similarities between bit streams coming from a network of motion detectors, we can recover the network geometry and discover structure in the human behavior being observed. This means that a low-cost network of sensors can provide powerful contextual information to building systems: improving the efficiency of elevators, lighting, heating, and cooling; enhancing safety and security; and opening up new opportunities for human-centered information systems. This paper will show how signal similarity can be used to calibrate a sensor network to accuracies below the resolution of the individual sensors. This is done by analyzing the similarity structures in the unconstrained movement of people in the observed space. We will also present our efficient behavior-learning algorithm that yields 90% correct behavior-detection in data from a sensor network comprised of motion detectors by employing similarity-based clustering to automatically decompose complex activities into detectable sub-classes.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2006.04.009