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

Citation
Jeremy Schiff. "Automated Intruder Tracking using Particle Filtering and a Network of Binary Motion Sensors". Talk or presentation, 2, November, 2006.

Abstract
Our objective is to automatically track and capture photos of an intruder using a robotic pan-tilt-zoom camera. In this talk, 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.

Electronic downloads

• jeremy-SNARE-TRUST-FINAL.ppt · application/vnd.ms-powerpoint · 2519 kbytes

Citation formats

• HTML

  Jeremy Schiff. <a
  href="http://www.truststc.org/pubs/134.html"
  ><i>Automated Intruder Tracking using Particle
  Filtering and a Network of Binary Motion
  Sensors</i></a>, Talk or presentation,  2,
  November, 2006.

• Plain text

  Jeremy Schiff. "Automated Intruder Tracking using
  Particle Filtering and a Network of Binary Motion
  Sensors". Talk or presentation,  2, November, 2006.

• BibTeX

  @presentation{Schiff06_AutomatedIntruderTrackingUsingParticleFilteringNetwork,
      author = {Jeremy Schiff},
      title = {Automated Intruder Tracking using Particle
                Filtering and a Network of Binary Motion Sensors},
      day = {2},
      month = {November},
      year = {2006},
      abstract = {Our objective is to automatically track and
                capture photos of an intruder using a robotic
                pan-tilt-zoom camera. In this talk, 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.},
      URL = {http://www.truststc.org/pubs/134.html}
  }
  

Posted by Christopher Brooks on 3 Nov 2006.
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