Skip navigation
Skip navigation

Ordered trajectories for human action recognition with large number of classes

Murthy, O.V. Ramana; Goecke, Roland

Description

Recently, a video representation based on dense trajectories has been shown to outperform other human action recognition methods on several benchmark datasets. The trajectories capture the motion characteristics of different moving objects in space and temporal dimensions. In dense trajectories, points are sampled at uniform intervals in space and time and then tracked using a dense optical flow field over a fixed length of L frames (optimally 15) spread overlapping over the entire video....[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Journal article
URI: http://hdl.handle.net/1885/76909
Source: Image and Vision Computing
DOI: 10.1016/j.imavis.2015.06.009

Download

File Description SizeFormat Image
01_Murthy_Ordered_trajectories_for_human_2015.pdf2.38 MBAdobe PDF    Request a copy


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

Updated:  12 November 2018/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator