Multi-target and Multi-camera Object Detection with Monte-Carlo Sampling

  • Authors:
  • Giorgio Panin;Sebastian Klose;Alois Knoll

  • Affiliations:
  • Fakultät für Informatik, Technische Universität München, Garching bei München, Germany 85748;Fakultät für Informatik, Technische Universität München, Garching bei München, Germany 85748;Fakultät für Informatik, Technische Universität München, Garching bei München, Germany 85748

  • Venue:
  • ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
  • Year:
  • 2009

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Abstract

In this paper, we propose a general-purpose methodology for detecting multiple objects with known visual models from multiple views. The proposed method is based Monte-Carlo sampling and weighted mean-shift clustering, and can make use of any model-based likelihood (color, edges, etc.), with an arbitrary camera setup. In particular, we propose an algorithm for automatic computation of the feasible state-space volume, where the particle set is uniformly initialized. We demonstrate the effectiveness of the method through simulated and real-world application examples.