Gaussian Process Regression Flow for Analysis of Motion Trajectories

Show full item record

Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/42261

Title: Gaussian Process Regression Flow for Analysis of Motion Trajectories
Author: Kim, Kihwan ; Lee, Dongryeol ; Essa, Irfan
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.
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. Presented at the 2011 IEEE International Conference on Computer Vision (ICCV), 6-13 November 2011, Barcelona, Spain. DOI: 10.1109/ICCV.2011.6126365
Type: Post-print
Proceedings
URI: http://hdl.handle.net/1853/42261
ISSN: 1550-5499
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.
Date: 2011-11
Contributor: Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Georgia Institute of Technology. College of Computing
Publisher: Georgia Institute of Technology
Institute of Electrical and Electronics Engineers
Subject: Flow fields
Gaussian process regression
Kurtosis
Motion trajectories
Tracking
Trajectory recognition

All materials in SMARTech are protected under U.S. Copyright Law and all rights are reserved, unless otherwise specifically indicated on or in the materials.

Files in this item

Files Size Format View
Gaussian Proces ... of Motion Trajectories.pdf 9.313Mb PDF View/ Open

This item appears in the following Collection(s)

Show full item record