Paper Info
Title
Smart Homecare Surveillance System: Behavior Identification Based on State-Transition Support Vector Machines and Sound Directivity Pattern Analysis.
Abstract
This study presents a smart homecare surveillance system, which utilizes sound-steered cameras to identify behavior of interest. First of all, to detect multiple source locations, a new direction-of-arrival (DOA) algorithm is proposed by introducing cascaded frequency filters, which can quickly calculate directions without creating much complexity. This method can also locate and separate different signals at the same time. Second, after the camera points in the direction of the estimated angle, the proposed state-transition support vector machine is used to provide favorable discriminability for human behavior identification. A new Markov random field (MRF) function based on the localized contour sequence (LCS) is also presented while the system computes transition probabilities between states. Such LCS-based MRF functions can effectively smooth transitions and enhance recognition. The experimental results show that the average error of DOA decreases to around 7, which is better than those of the baselines. Also, our proposed behavior identification system can reach an 88.3% accuracy rate. The aforementioned results have therefore demonstrated the feasibility of the proposed method.
Year
DOI
Venue
2013
10.1109/TSMC.2013.2244211
IEEE T. Systems, Man, and Cybernetics: Systems
Keywords
Field
DocType
Hidden Markov models,Support vector machines,Pattern analysis,Ambient intelligence,Surveillance,Direction-of-arrival estimation
Markov process,Pattern recognition,Markov random field,Computer science,Support vector machine,Identification system,Stochastic process,Home automation,Artificial intelligence,Directivity pattern,Filtering theory,Machine learning
Journal
Volume
Issue
ISSN
43
6
2168-2216
Citations 
PageRank 
References 
15
0.63
0
Authors
3
Name
Order
Citations
PageRank
Bo-Wei Chen126230.12
Chen-Yu Chen2526.35
Jhing-fa Wang3982114.31