Discrimination of bipeds from quadrupeds using seismic footstep signatures

Seismic sensors are widely used to detect moving targets in the ground sensor network, and can be easily employed to discriminate human and quadruped based on their footstep signatures. Because of the complex environmental conditions and the non-stationary nature of the seismic signals, footstep detection and classification is a very challenging problem. The solution to this problem has various applications such as border security, surveillance, perimeter protection and intruder detection. Previous works in the domain of seismic detection of human vs. quadruped have relied on the cadence frequency-based models. However, cadence-based detection alone results in high false alarms. In this paper, we describe a seismic footstep database and present classification results based on support vector machine (SVM). We demonstrate that in addition to applying a good classification algorithm, finding robust features are very important for seismic discrimination.