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Multi-Sensor Signal based Situation Recognition with Bayesian Networks
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 Title & Authors
Multi-Sensor Signal based Situation Recognition with Bayesian Networks
Kim, Jin-Pyung; Jang, Gyu-Jin; Jung, Jae-Young; Kim, Moon-Hyun;
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In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.
Structure learning;Bayesian networks;Multiple sensor signals;K2-learning algorithm;UCI machine learning repository;
 Cited by
Crowd Activity Recognition using Optical Flow Orientation Distribution,Kim, Jinpyung;Jang, Gyujin;Kim, Gyujin;Kim, Moon-Hyun;

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