Coverage Estimation in the Presence of Occlusions for Visual Sensor Networks

  • Cheng Qian
  • Hairong Qi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5067)


Visual coverage is an essential issue in the research on visual sensor networks. However, because of the presence of visual occlusions, the statistics of visual coverage blend the statistics of nodes and targets and are extremely difficult to derive. By assuming the deployment of nodes as a stationary Poisson point process and ignoring boundary effects, this paper presents the first attempt to estimate the probability that an arbitrary target in the field is visually k-covered. The major challenge for the estimation is how to formulate the probability (q) that a node captures a target in its visual range. To tackle this challenge, we first assume a visual detection model that takes visual occlusions into account and then derive several significant statistical parameters of q based on this model. According to these parameters, we can finally reconstruct the probability density function of q as a combination of a Binomial function and an impulse function. With the estimated coverage statistics, we further propose an estimate of the minimum node density that suffices to ensure a K-coverage across the field.


Sensor Network Wireless Sensor Network Node Density Poisson Point Process Target Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cheng Qian
    • 1
  • Hairong Qi
    • 2
  1. 1.Viatronix Inc, Stony BrookNew York 
  2. 2.University of TennesseeKnoxville 

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