DocumentCode :
2203895
Title :
Smart home automation system for intrusion detection
Author :
Chowdhry, Danish ; Paranjape, Raman ; Laforge, Paul
Author_Institution :
Faculty of Engineering and Applied Science University of Regina Regina, Canada
fYear :
2015
fDate :
6-9 July 2015
Firstpage :
75
Lastpage :
78
Abstract :
In the area of home security and protection, vision-based home automation systems (HAS) play a significant role. In this paper, we present the design and implementation of a smart HAS with incorporated intrusion detection to minimize damages caused by burglary. In addition, the proposed HAS integrates a web server to home appliances in order to remotely access and control their status. Intrusion detection, on the other hand, uses Histogram of Oriented Gradients (HOG) feature descriptors and a Support Vector Machine (SVM) classifier for accurate human detection by smartly rejecting false alarms arising from pets. This system comprised of a simple architecture and requires no human intervention. It allows for prompt accessibility, efficient usage of electricity and provides user convenience.
Keywords :
Feature extraction; Home appliances; Home automation; Intrusion detection; Positron emission tomography; Support vector machines; Web servers; Home automation; Internet; appliances; histogram of oriented gradients; intrusion detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Theory (CWIT), 2015 IEEE 14th Canadian Workshop on
Conference_Location :
St. John´s, NL, Canada
Type :
conf
DOI :
10.1109/CWIT.2015.7255156
Filename :
7255156
Link To Document :
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