Activity Recognition with Anomaly Detection Using Non-Parametric Topic Modeling
Date Issued
2015
Date
2015
Author(s)
Wang, Shih-Han
Abstract
In the era of simple-heterogeneous-environmental(SHE) sensors, activity recognition plays an important role in many applications, such as recognizing normal activity and detecting abnormal activity. However, most research was conducted for either one but lost generality to both. We, therefore, start by developing higher-level representations for any qualified model to perform prediction as apposed to designing a specific model to fit certain data. In this thesis, we proposed a framework by non-parametric topic modeling to extract pattern as a higher-level representation from 5-month SHE sensor data. Our method can be used in a room with multiple sensors deployed and allow sensor fusion. In experiment, we have qualitative results and quantitative results. In our qualitative results, we generated visualized patterns for humans to detect abnormal activity. As a result, we successfully detect an intruder who was once reported on the news. In the quantitative evaluation, we demonstrate that the proposed framework can significantly compress sensor data over 99% and achieve best performance over 87% of accuracy on average in activity recognition. In anomaly detection, the proposed framework can effectively extract descriptive patterns for one-class SVM to detect abnormal activity. To summarize, we have shown the effective results of the proposed framework and use it to solve a real world problem, anomaly detection in a room.
Subjects
Non-parametric topic model
anomaly detection
activity recognition
Type
thesis