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An occlusion metric for selecting robust camera configurations

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Abstract

Vision based tracking systems for surveillance and motion capture rely on a set of cameras to sense the environment. The exact placement or configuration of these cameras can have a profound affect on the quality of tracking which is achievable. Although several factors contribute, occlusion due to moving objects within the scene itself is often the dominant source of tracking error. This work introduces a configuration quality metric based on the likelihood of dynamic occlusion. Since the exact geometry of occluders can not be known a priori, we use a probabilistic model of occlusion. This model is extensively evaluated experimentally using hundreds of different camera configurations and found to correlate very closely with the actual probability of feature occlusion.

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Authors and Affiliations

  1. NVIDIA Corporation, Santa Clara, CA, USA

    Xing Chen

  2. University of California - Santa Cruz, Santa Cruz, CA, USA

    James Davis

Authors
  1. Xing Chen
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  2. James Davis
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Correspondence to James Davis.

Additional information

Authors X. Chen and J. Davis were in Computer Graphics Lab at Stanford University at time of research.

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Chen, X., Davis, J. An occlusion metric for selecting robust camera configurations. Machine Vision and Applications 19, 217–222 (2008). https://doi.org/10.1007/s00138-007-0094-y

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  • DOI: https://doi.org/10.1007/s00138-007-0094-y

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