Acoustic Surveillance Intrusion Detection with Linear Predictive Coding and Random Forest

Abstract

Endangered wildlife is protected in remote land where people are restricted to enter. But intrusions of poachers and illegal loggers still occur due to lack of surveillance to cover a huge amount of land. The current usage of stealth ability of the camera is low due to limitations of camera angle of view. Maintenance such as changing batteries and memory cards were troublesome reported by Wildlife Conservation Society, Malaysia. Remote location with no cellular network access would be difficult to transmit video data. Rangers need a system to react to intrusion on time. This paper aims to address the development of an audio events recognition for intrusion detection based on the vehicle engine, wildlife environmental noise and chainsaw activities. Random Forest classification and feature extraction of Linear Predictive Coding were employed. Training and testing data sets used were obtained from Wildlife Conservation Society Malaysia. The findings demonstrate that the accuracy rates achieve up to 86% for indicating an intrusion via audio recognition. It is a good attempt as a primary study for the classification of a real data set of intruders. This intrusion detection will be beneficial for wildlife protection agencies in maintaining security as it is less power consuming than the current camera trapping surveillance technique.