Title

Detection Of Independently Moving Objects In Non-Planar Scenes Via Multi-Frame Monocular Epipolar Constraint

Abstract

In this paper we present a novel approach for detection of independently moving foreground objects in non-planar scenes captured by a moving camera. We avoid the traditional assumptions that the stationary background of the scene is planar, or that it can be approximated by dominant single or multiple planes, or that the camera used to capture the video is orthographic. Instead we utilize a multiframe monocular epipolar constraint of camera motion derived for monocular moving cameras defined by an evolving epipolar plane between the moving camera center and 3D scene points. This constraint is parameterized as a polynomial function of time, and unlike repeated computations of inter-frame fundamental matrix, requires the estimation of fewer unknowns, and provides a more consistent separation between moving and static objects for different levels of noise. This constraint allows us to segment out moving objects in a general 3D scene where other approaches fail because their initial assumptions do not hold, and provides a natural way of fusing temporal information across multiple frames. We use a combination of optical flow and particle advection to capture all motion in the video across a number of frames, in the form of particle trajectories. We then apply the derived multi-frame epipolar constraint to these trajectories to determine which trajectories violate it, thus segmenting out the independently moving objects. We show superior results on a number of moving camera sequences observing non-planar scenes, where other methods fail. © 2012 Springer-Verlag.

Publication Date

10-30-2012

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

7576 LNCS

Issue

PART 5

Number of Pages

860-873

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-33715-4_62

Socpus ID

84867848595 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84867848595

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