Title:
Visual Place Categorization: Problem, Dataset, and Algorithm
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) | |
relation.isAuthorOfPublication | af5b46ec-ffe2-4ce4-8722-1373c9b74a37 | |
relation.isAuthorOfPublication | afdc727f-2705-4744-945f-e7d414f2212b | |
relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |