Robust 3D segmentation of multiple moving objects under weak perspective

  • Authors:

  • Levente Hajder;Dmitry Chetverikov

  • Affiliations:

  • Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary and Budapest University of Technology and Economics, Department of Automation and Applied Informatics;Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary and Eötvös Loránd University

  • Venue:
  • WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
  • Year:
  • 2006

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Abstract

A scene containing multiple independently moving, possibly occluding, rigid objects is considered under the weak perspective camera model. We obtain a set of feature points tracked across a number of frames and address the problem of 3D motion segmentation of the objects in presence of measurement noise and outliers. We extend the robust structure from motion (SfM) method [5] to 3D motion segmentation and apply it to realistic, contaminated tracking data with occlusion. A number of approaches to 3D motion segmentation have already been proposed [3, 6, 14, 15]. However, most of them were not developed for, and tested on, noisy and outlier-corrupted data that often occurs in practice. Due to the consistent use of robust techniques at all critical steps, our approach can cope with such data, as demonstrated in a number of tests with synthetic and real image sequences.