Abnormal event detection using Bayesian networks at a smart home

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.