Automated Intruder Tracking using Particle Filtering and a Network of Binary Motion Sensors

Our objective is to automatically track and capture photos of an intruder using a robotic pan-tilt-zoom camera. In this paper, we consider the problem of automated position estimation using a wireless network of inexpensive binary motion sensors. The challenge is to incorporate data from a network of noisy sensors that suffer from refractory periods during which they may be unresponsive. We propose an estimation method based on particle filtering, a numerical sequential Monte Carlo technique. We model sensors with conditional probability density functions and incorporate a probabilistic model of an intruder´s state that utilizes velocity. We present simulation and experiments with passive infrared (PIR) motion sensors that suggest that our estimator is effective and degrades gracefully with increasing sensor refractory periods.