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1077-2014/98/040125 + 17 $25.00/ri970075 1998 Academic Press Limited Robust Regression for the Detection of

Summary: 1077-2014/98/040125 + 17 $25.00/ri970075 1998 Academic Press Limited
Robust Regression for the Detection of
Independent 3D Motion by a Binocular
method is proposed for the visual detection of objects that move independently of the
observer in a 3D dynamic environment. Many of the existing techniques for solving this
Aproblem are based on 2D motion models, which is equivalent to assuming that all the
objects in a scene are at a constant depth from the observer. Although such methods perform well if
this assumption holds, they may give erroneous results when applied to scenes with large depth
variations. Additionally, many of the existing techniques rely on the computation of optical flow,
which amounts to solving the ill-posed correspondence problem. In this paper, independent 3D
motion detection is formulated using 3D models and is approached as a problem of robust regres-
sion applied to visual input acquired by a binocular, rigidly moving observer. Similar analysis is
applied both to the stereoscopic data taken by a non-calibrated stereoscopic system and to the
motion data obtained from successive frames in time. Least Median of Squares (LMedS) estima-
tion is applied to stereoscopic data to produce maps of image regions characterized by a dominant
depth. LMedS is also applied to the motion data that are related to the points at the dominant depth,
to segment the latter with respect to 3D motion. In contrast to the methods that rely on 2D models,
the proposed method performs accurately, even in the case of scenes with large depth variations.
Both stereo and motion processing is based on the normal flow field, which (in contrast to the opti-


Source: Argyros, Antonis - Foundation of Research and Technology, Hellas & Department of Computer Science, University of Crete
Trahanias, Panos - Institute of Computer Science, Foundation of Research and Technology, Hellas


Collections: Computer Technologies and Information Sciences