NDLI: Abnormal event detection using Bayesian networks at a smart home

Content Provider	IEEE Xplore Digital Library
Author	Yu-Ling Hsueh Nien-Hung Lin Chia-Che Chang Chen, O.T.-C. Wen-Nung Lie
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Nat. Chung Cheng Univ., Chiayi, Taiwan (Chen, O.T.-C.; Wen-Nung Lie) \|\| Dept. of Comput. Sci. & Inf. Eng., Nat. Chung Cheng Univ., Chiayi, Taiwan (Yu-Ling Hsueh; Nien-Hung Lin; Chia-Che Chang)
Abstract	Existing methods have addressed the issue of detecting abnormal events at a smart home for medical care or security monitoring services extensively in the past decades. However, most of approaches use wearable sensors that require users to be equipped with the sensor devices at every moment. If the monitored users stop or pause the sensors, any abnormal events are not able to be detected. The use of non-wearable and non-intrusive sensors (e.g., IP cameras) is necessary for providing better user experiences and achieving sustainable and reliable detection model. However, it is still very challenging to analyze such non-wearable sensor data with a high accuracy. In this work, we propose an event detection model using a Bayesian Network. We first obtain the features by analyzing the daily videos and audios captured from different angles by multiple IP cameras at a smart home. These features are then used to construct a Bayesian network. We propose a probabilistic graph model where the dependence relations are defined in the graph as opposed to the naive Bayesian network. The experiments are presented to demonstrate the performance and utility of our model.
Starting Page	273
Ending Page	277
File Size	1841994
Page Count	5
File Format	PDF
e-ISBN	9781467382700
DOI	10.1109/UMEDIA.2015.7297468
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-24
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian Networks Smart Home Hidden Markov models Abnormal Event Detection Smart homes Classification Cameras Data models Bayes methods Trajectory IP networks
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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