Team for Research in
Ubiquitous Secure Technology

Citation
Jeremy Schiff, Ken Goldberg. "Automated Intruder Tracking using Particle Filtering and a Network of Binary Motion Sensors.". IEEE International Conference on Automation Science and Engineering (CASE)., October, 2006.

Abstract
Our objective is to automatically track and capture photos of an intruder using a robotic pan-tiltzoom 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.

Electronic downloads

• CASE-2006.pdf · application/pdf · 327 kbytes

• HTML

  Jeremy Schiff, Ken Goldberg. <a
  href="http://www.truststc.org/pubs/708.html"
  >Automated Intruder Tracking using Particle Filtering and
  a Network of Binary Motion Sensors.</a>, IEEE
  International Conference on Automation Science and
  Engineering (CASE)., October, 2006.

• Plain text

  Jeremy Schiff, Ken Goldberg. "Automated Intruder
  Tracking using Particle Filtering and a Network of Binary
  Motion Sensors.". IEEE International Conference on
  Automation Science and Engineering (CASE)., October, 2006.

• BibTeX

  @inproceedings{SchiffGoldberg06_AutomatedIntruderTrackingUsingParticleFilteringNetwork,
      author = {Jeremy Schiff and Ken Goldberg},
      title = {Automated Intruder Tracking using Particle
                Filtering and a Network of Binary Motion Sensors.},
      booktitle = {IEEE International Conference on Automation
                Science and Engineering (CASE).},
      month = {October},
      year = {2006},
      abstract = {Our objective is to automatically track and
                capture photos of an intruder using a robotic
                pan-tiltzoom 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.},
      URL = {http://www.truststc.org/pubs/708.html}
  }
  

Posted by Jessica Gamble on 5 Apr 2010.
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