An Efficient Gaussian Mixture Model Classifier for Outdoor Surveillance Using Seismic Signals

For surveillance of high-security zones, seismic sensors have received considerable attention in numerous civilian and military applications. Since seismic sensors are highly sensitive to the Earth’s surface vibration, outdoor surveillance is difficult during seasonal variations. We acquire empirical seismic signals of various events during normal and rainy weather over a period of three months and analyze the potential challenges in the classification of intrusion with existing solutions. In this work, we propose to categorize events occurring during rain as a separate class to improve upon classification accuracies for human, vehicle, animal movements, and no-disturbance in normal as well as rainy weather. Next, considering the similarities in speech and seismic signals, we propose Gaussian mixture modeling of Mel-frequency cepstral coefficients (MFCCs) and their first derivatives (
$\Delta $
-MFCCs) of seismic signals followed by maximum likelihood estimation of the corresponding Gaussian mixture model (GMM) of the true event. We conduct extensive experiments with a setup in an outdoor environment and demonstrate that consideration of rain as a separate event greatly enhances the efficacy of the classifier. We provide a comparative performance analysis and show that the proposed classifier with an overall accuracy of 97.7% in normal and rainy weather outperforms existing solutions, popular machine learning (ML), and deep learning (DL) models.