Title:
Visual Place Categorization: Problem, Dataset, and Algorithm

dc.contributor.author Wu, Jianxin en_US
dc.contributor.author Rehg, James M. en_US
dc.contributor.author Christensen, Henrik I. en_US
dc.contributor.corporatename Georgia Institute of Technology. College of Computing en_US
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines en_US
dc.date.accessioned 2011-03-14T19:03:22Z
dc.date.available 2011-03-14T19:03:22Z
dc.date.issued 2009-10
dc.description (c) 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/RSJ International Conference on Intelligent Robots and Systems; October 11-15, 2009; St. Louis, USA.
dc.description Digital Object Identifier: 10.1109/IROS.2009.5354164
dc.description.abstract In this paper we describe the problem of Visual Place Categorization (VPC) for mobile robotics, which involves predicting the semantic category of a place from image measurements acquired from an autonomous platform. For example, a robot in an unfamiliar home environment should be able to recognize the functionality of the rooms it visits, such as kitchen, living room, etc. We describe an approach to VPC based on sequential processing of images acquired with a conventional video camera.We identify two key challenges: Dealing with non-characteristic views and integrating restricted-FOV imagery into a holistic prediction. We present a solution to VPC based upon a recently-developed visual feature known as CENTRIST (CENsus TRansform hISTogram). We describe a new dataset for VPC which we have recently collected and are making publicly available. We believe this is the first significant, realistic dataset for the VPC problem. It contains the interiors of six different homes with ground truth labels. We use this dataset to validate our solution approach, achieving promising results. en_US
dc.identifier.citation Wu, J., Rehg, J., and Christensen, H. I. Visual Place Categorization: Problem, Dataset, and Algorithm. In IEEE/RSJ Intl. Conf. on Intell. Robots and Systems (St. Louis, MO, Oct. 2009), IEEE. en_US
dc.identifier.doi 10.1109/IROS.2009.5354164
dc.identifier.isbn 978-1-4244-3803-7
dc.identifier.uri http://hdl.handle.net/1853/37387
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Institute of Electrical and Electronics Engineers en_US
dc.subject Mobile robots en_US
dc.subject Datasets en_US
dc.subject Visual place categorization en_US
dc.title Visual Place Categorization: Problem, Dataset, and Algorithm en_US
dc.type Text
dc.type.genre Article
dspace.entity.type Publication
local.contributor.author Rehg, James M.
local.contributor.author Christensen, Henrik I.
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
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relation.isAuthorOfPublication afdc727f-2705-4744-945f-e7d414f2212b
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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