Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICME.2010.5583046
Title: Activity recognition using dense long-duration trajectories
Authors: Sun, J. 
Mu, Y. 
Yan, S. 
Cheong, L.-F. 
Keywords: Action recognition
Computer vision
Motion trajectories
Motion understanding
Tracking
Video analysis
Issue Date: 2010
Source: Sun, J., Mu, Y., Yan, S., Cheong, L.-F. (2010). Activity recognition using dense long-duration trajectories. 2010 IEEE International Conference on Multimedia and Expo, ICME 2010 : 322-327. ScholarBank@NUS Repository. https://doi.org/10.1109/ICME.2010.5583046
Abstract: Current research on visual action/activity analysis has mostly exploited appearance-based static feature descriptions, plus statistics of short-range motion fields. The deliberate ignorance of dense, long-duration motion trajectories as features is largely due to the lack of mature mechanism for efficient extraction and quantitative representation of visual trajectories. In this paper, we propose a novel scheme for extraction and representation of dense, long-duration trajectories from video sequences, and demonstrate its ability to handle video sequences containing occlusions, camera motions, and nonrigid deformations. Moreover, we test the scheme on the KTH action recognition dataset [1], and show its promise as a scheme for general purpose long-duration motion description in realistic video sequences. © 2010 IEEE.
Source Title: 2010 IEEE International Conference on Multimedia and Expo, ICME 2010
URI: http://scholarbank.nus.edu.sg/handle/10635/69154
ISBN: 9781424474912
DOI: 10.1109/ICME.2010.5583046
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

24
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

11
checked on Nov 20, 2017

Page view(s)

48
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.