The influence of temporal information on human action recognition with large number of classes

Ramana ORUGANTI, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Citations (Scopus)

Abstract

Human action recognition from video input has seen much interest over the last decade. In recent years, the trend is clearly towards action recognition in real-world, unconstrained conditions (i.e. not acted) with an ever growing number of action classes. Much of the work so far has used single frames or sequences of frames where each frame was treated individually. This paper investigates the contribution that temporal information can make to human action recognition in the context of a large number of action classes. The key contributions are: (i) We propose a complementary information channel to the Bag-of- Words framework that models the temporal occurrence of the local information in videos. (ii) We investigate the influence of sensible local information whose temporal occurrence is more vital than any local information. The experimental validation on action recognition datasets with the largest number of classes to date shows the effectiveness of the proposed approach.
Original languageEnglish
Title of host publication2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014)
Subtitle of host publicationTechniques and Applications, DICTA 2014
EditorsSon Lam Phung, Abdesselam Bouzerdoum, Philip Ogunbona, Wanqing Li, Lei Wang
Place of PublicationWollongong Austraia
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9781479954094
ISBN (Print)9781479954100
DOIs
Publication statusPublished - 25 Nov 2014
Event2014 International Conference on Digital Image Computing, Techniques and Applications - Wollongong, Wollongong, Australia
Duration: 25 Nov 201427 Nov 2014

Publication series

Name2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014

Conference

Conference2014 International Conference on Digital Image Computing, Techniques and Applications
Abbreviated titleDICTA 2014
CountryAustralia
CityWollongong
Period25/11/1427/11/14

Cite this

ORUGANTI, R., & GOECKE, R. (2014). The influence of temporal information on human action recognition with large number of classes. In S. L. Phung, A. Bouzerdoum, P. Ogunbona, W. Li, & L. Wang (Eds.), 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014): Techniques and Applications, DICTA 2014 (pp. 1-8). [7008131] (2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014). Wollongong Austraia: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2014.7008131