Distributed consensus on camera pose

Anne Jorstad, Daniel Dementhon, I. Jeng Wang, Philippe Burlina

Research output: Contribution to journalArticlepeer-review

Abstract

Our work addresses pose estimation in a distributed camera framework. We examine how processing cameras can best reach a consensus about the pose of an object when they are each given a model of the object, defined by a set of point coordinates in the object frame of reference. The cameras can only see a subset of the object feature points in the midst of background clutter points, not knowing which image points match with which object points, nor which points are object points or background points. The cameras individually recover a prediction of the object's pose using their knowledge of the model, and then exchange information with their neighbors, performing consensus updates locally to obtain a single estimate consistent across all cameras, without requiring a common centralized processor. Our main contributions are: 1) we present a novel algorithm performing consensus updates in 3-D world coordinates penalized by a 3-D model, and 2) we perform a thorough comparison of our method with other current consensus methods. Our method is consistently the most accurate, and we confirm that the existing consensus method based upon calculating the Karcher mean of rotations is also reliable and fast. Experiments on simulated and real imagery are reported.

Original languageEnglish (US)
Article number5440905
Pages (from-to)2396-2407
Number of pages12
JournalIEEE Transactions on Image Processing
Volume19
Issue number9
DOIs
StatePublished - Sep 1 2010

Keywords

  • Computer vision
  • Karcher mean
  • SoftPOSIT
  • distributed computing
  • iterative pose estimation
  • multiple view geometry
  • multiview geometry
  • object model
  • penalized consensus
  • sensor fusion
  • video surveillance

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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