Title:
Gaussian Process Regression Flow for Analysis of Motion Trajectories

dc.contributor.author Kim, Kihwan
dc.contributor.author Lee, Dongryeol
dc.contributor.author Essa, Irfan
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 2012-01-19T21:00:57Z
dc.date.available 2012-01-19T21:00:57Z
dc.date.issued 2011-11
dc.description ©2011 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 2011 IEEE International Conference on Computer Vision (ICCV), 6-13 November 2011, Barcelona, Spain.
dc.description DOI: 10.1109/ICCV.2011.6126365
dc.description.abstract Recognition of motions and activities of objects in videos requires effective representations for analysis and matching of motion trajectories. In this paper, we introduce a new representation specifically aimed at matching motion trajectories. We model a trajectory as a continuous dense flow field from a sparse set of vector sequences using Gaussian Process Regression. Furthermore, we introduce a random sampling strategy for learning stable classes of motions from limited data. Our representation allows for incrementally predicting possible paths and detecting anomalous events from online trajectories. This representation also supports matching of complex motions with acceleration changes and pauses or stops within a trajectory. We use the proposed approach for classifying and predicting motion trajectories in traffic monitoring domains and test on several data sets. We show that our approach works well on various types of complete and incomplete trajectories from a variety of video data sets with different frame rates. en_US
dc.identifier.citation Kim, K., Lee, D., & Essa, I. (2011). "Gaussian Process Regression Flow for Analysis of Motion Trajectories". Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV), 6-13 November 2011, pp.1164-1171. en_US
dc.identifier.issn 1550-5499
dc.identifier.uri http://hdl.handle.net/1853/42261
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 Flow fields en_US
dc.subject Gaussian process regression en_US
dc.subject Kurtosis en_US
dc.subject Motion trajectories en_US
dc.subject Tracking en_US
dc.subject Trajectory recognition en_US
dc.title Gaussian Process Regression Flow for Analysis of Motion Trajectories en_US
dc.type Text
dc.type.genre Post-print
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Essa, Irfan
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
relation.isAuthorOfPublication 84ae0044-6f5b-4733-8388-4f6427a0f817
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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