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
ORUGANTI, Ramana ; GOECKE, Roland. / The influence of temporal information on human action recognition with large number of classes. 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014): Techniques and Applications, DICTA 2014. editor / Son Lam Phung ; Abdesselam Bouzerdoum ; Philip Ogunbona ; Wanqing Li ; Lei Wang. Wollongong Austraia : IEEE, Institute of Electrical and Electronics Engineers, 2014. pp. 1-8 (2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014).
@inproceedings{5293737bf3f64b07932c3441492d5a5b,
title = "The influence of temporal information on human action recognition with large number of classes",
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.",
keywords = "Human Action Recognition, Temporal information, Bag of Words",
author = "Ramana ORUGANTI and Roland GOECKE",
year = "2014",
month = "11",
day = "25",
doi = "10.1109/DICTA.2014.7008131",
language = "English",
isbn = "9781479954100",
series = "2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--8",
editor = "Phung, {Son Lam} and Abdesselam Bouzerdoum and Philip Ogunbona and Wanqing Li and Lei Wang",
booktitle = "2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014)",
address = "United States",

}

ORUGANTI, R & GOECKE, R 2014, The influence of temporal information on human action recognition with large number of classes. in SL 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., 7008131, 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, IEEE, Institute of Electrical and Electronics Engineers, Wollongong Austraia, pp. 1-8, 2014 International Conference on Digital Image Computing, Techniques and Applications, Wollongong, Australia, 25/11/14. https://doi.org/10.1109/DICTA.2014.7008131

The influence of temporal information on human action recognition with large number of classes. / ORUGANTI, Ramana; GOECKE, Roland.

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014): Techniques and Applications, DICTA 2014. ed. / Son Lam Phung; Abdesselam Bouzerdoum; Philip Ogunbona; Wanqing Li; Lei Wang. Wollongong Austraia : IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 1-8 7008131 (2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014).

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

TY - GEN

T1 - The influence of temporal information on human action recognition with large number of classes

AU - ORUGANTI, Ramana

AU - GOECKE, Roland

PY - 2014/11/25

Y1 - 2014/11/25

N2 - 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.

AB - 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.

KW - Human Action Recognition

KW - Temporal information

KW - Bag of Words

UR - http://www.scopus.com/inward/record.url?scp=84922572022&partnerID=8YFLogxK

U2 - 10.1109/DICTA.2014.7008131

DO - 10.1109/DICTA.2014.7008131

M3 - Conference contribution

SN - 9781479954100

T3 - 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014

SP - 1

EP - 8

BT - 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2014)

A2 - Phung, Son Lam

A2 - Bouzerdoum, Abdesselam

A2 - Ogunbona, Philip

A2 - Li, Wanqing

A2 - Wang, Lei

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Wollongong Austraia

ER -

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