NDLI: Dense multibody motion estimation and reconstruction from a handheld camera

Content Provider	IEEE Xplore Digital Library
Author	Roussos, A. Russell, C. Garg, R. Agapito, L.
Copyright Year	2012
Description	Author affiliation: School of Electronic Engineering and Computer Science, Queen Mary University of London, UK (Roussos, A.; Russell, C.; Garg, R.; Agapito, L.)
Abstract	Existing approaches to camera tracking and reconstruction from a single handheld camera for Augmented Reality (AR) focus on the reconstruction of static scenes. However, most real world scenarios are dynamic and contain multiple independently moving rigid objects. This paper addresses the problem of simultaneous segmentation, motion estimation and dense 3D reconstruction of dynamic scenes. We propose a dense solution to all three elements of this problem: depth estimation, motion label assignment and rigid transformation estimation directly from the raw video by optimizing a single cost function using a hill-climbing approach. We do not require prior knowledge of the number of objects present in the scene — the number of independent motion models and their parameters are automatically estimated. The resulting inference method combines the best techniques in discrete and continuous optimization: a state of the art variational approach is used to estimate the dense depth maps while the motion segmentation is achieved using discrete graph-cut based optimization. For the rigid motion estimation of the independently moving objects we propose a novel tracking approach designed to cope with the small fields of view they induce and agile motion. Our experimental results on real sequences show how accurate segmentations and dense depth maps can be obtained in a completely automated way and used in marker-free AR applications.
Starting Page	31
Ending Page	40
File Size	16347359
Page Count	10
File Format	PDF
ISBN	9781467346603
e-ISBN	9781467346627
e-ISBN	9781467346610
DOI	10.1109/ISMAR.2012.6402535
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cameras Optimization Motion segmentation Image reconstruction Tracking Estimation Motion estimation
Content Type	Text
Resource Type	Article

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