Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies

Roberts, Richard; Potthast, Christian; Dellaert, Frank

Title:

Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies

dc.contributor.author	Roberts, Richard
dc.contributor.author	Potthast, Christian
dc.contributor.author	Dellaert, Frank
dc.contributor.corporatename	Georgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatename	Georgia Institute of Technology. College of Computing
dc.date.accessioned	2011-03-30T20:49:23Z
dc.date.available	2011-03-30T20:49:23Z
dc.date.issued	2009
dc.description	©2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.	en_US
dc.description	Presented at the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 20-25 June 2009, Miami, FL.
dc.description	DOI: 10.1109/CVPR.2009.5206538
dc.description.abstract	This paper deals with estimation of dense optical flow and ego-motion in a generalized imaging system by exploiting probabilistic linear subspace constraints on the flow. We deal with the extended motion of the imaging system through an environment that we assume to have some degree of statistical regularity. For example, in autonomous ground vehicles the structure of the environment around the vehicle is far from arbitrary, and the depth at each pixel is often approximately constant. The subspace constraints hold not only for perspective cameras, but in fact for a very general class of imaging systems, including catadioptric and multiple-view systems. Using minimal assumptions about the imaging system, we learn a probabilistic subspace constraint that captures the statistical regularity of the scene geometry relative to an imaging system. We propose an extension to probabilistic PCA (Tipping and Bishop, 1999) as a way to robustly learn this subspace from recorded imagery, and demonstrate its use in conjunction with a sparse optical flow algorithm. To deal with the sparseness of the input flow, we use a generative model to estimate the subspace using only the observed flow measurements. Additionally, to identify and cope with image regions that violate subspace constraints, such as moving objects, objects that violate the depth regularity, or gross flow estimation errors, we employ a per-pixel Gaussian mixture outlier process. We demonstrate results of finding the optical flow subspaces and employing them to estimate dense flow and to recover camera motion for a variety of imaging systems in several different environments.	en_US
dc.identifier.citation	Roberts, R., Potthast, C., & Dellaert, F. (2009). “Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, 57-64.	en_US
dc.identifier.issn	1063-6919
dc.identifier.uri	http://hdl.handle.net/1853/38341
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.publisher.original	Institute of Electrical and Electronics Engineers
dc.subject	Egomotion estimation	en_US
dc.subject	Images	en_US
dc.subject	Linear subspace	en_US
dc.subject	Optical flow estimation	en_US
dc.subject	Probabilistic PCA	en_US
dc.title	Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies	en_US
dc.type	Text
dc.type.genre	Post-print
dc.type.genre	Proceedings
dspace.entity.type	Publication
local.contributor.author	Dellaert, Frank
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
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relation.isOrgUnitOfPublication	66259949-abfd-45c2-9dcc-5a6f2c013bcf