Video characterization based on activity clustering

Nikolaos Kourous; Alexandros Iosifidis; Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas

doi:10.1109/ICECE.2014.7026876

Video characterization based on activity clustering

Nikolaos Kourous, Alexandros Iosifidis, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas

Department of Electrical & Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

6 Citations (Scopus)

313 Downloads (Pure)

Abstract

In this paper, we propose an efficient method for video characterization based on activity information. We employ a state-of-the-art video representation in order to learn human activity concepts, i.e., video groups formed by videos depicting
similar human activities. In order to exploit the enriched visual information that is available in multi-view settings, we propose the use of the circular shift invariance property of the coefficients of the Discrete Fourier Transform (DFT) that leads to a view-independent multi-view action representation. In the test phase, in order to assign a test video to one (or multiple) activity groups, we perform temporal video segmentation in order to determine shorter videos depicting simple actions. Experimental results on the i3DPost multi-view action database and a new multi-view action database denote the effectiveness of the proposed approach.

Original language	English
Title of host publication	2014 International Conference on Electrical and Computer Engineering (ICECE 2014)
Subtitle of host publication	Proceedings of a meeting held 20-22 December 2014, Dhaka, Bangladesh
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	266-269
Number of pages	4
ISBN (Electronic)	9781479941674
ISBN (Print)	9781479941650
DOIs	https://doi.org/10.1109/ICECE.2014.7026876
Publication status	Published - Mar 2015
Event	International Conference on Electrical and Computer Engineering (ICECE) - Dhaka, Bangladesh Duration: 20 Dec 2014 → 22 Dec 2014

Conference

Conference	International Conference on Electrical and Computer Engineering (ICECE)
Country/Territory	Bangladesh
City	Dhaka
Period	20/12/14 → 22/12/14

Keywords

Video characterization
Activity clustering
Multi-camera setup

Access to Document

10.1109/ICECE.2014.7026876

Ioannis Pitas - Video Characterization based on Activity Clustering - 2014
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7026876. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 280 KB

Handle.net

Persistent link

Cite this

Kourous, N., Iosifidis, A., Tefas, A., Nikolaidis, N., & Pitas, I. (2015). Video characterization based on activity clustering. In 2014 International Conference on Electrical and Computer Engineering (ICECE 2014): Proceedings of a meeting held 20-22 December 2014, Dhaka, Bangladesh (pp. 266-269). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICECE.2014.7026876

@inproceedings{8790ade734a94fa685761086ee1cf7f9,

title = "Video characterization based on activity clustering",

abstract = "In this paper, we propose an efficient method for video characterization based on activity information. We employ a state-of-the-art video representation in order to learn human activity concepts, i.e., video groups formed by videos depictingsimilar human activities. In order to exploit the enriched visual information that is available in multi-view settings, we propose the use of the circular shift invariance property of the coefficients of the Discrete Fourier Transform (DFT) that leads to a view-independent multi-view action representation. In the test phase, in order to assign a test video to one (or multiple) activity groups, we perform temporal video segmentation in order to determine shorter videos depicting simple actions. Experimental results on the i3DPost multi-view action database and a new multi-view action database denote the effectiveness of the proposed approach.",

keywords = "Video characterization, Activity clustering, Multi-camera setup",

author = "Nikolaos Kourous and Alexandros Iosifidis and Anastasios Tefas and Nikos Nikolaidis and Ioannis Pitas",

year = "2015",

month = mar,

doi = "10.1109/ICECE.2014.7026876",

language = "English",

isbn = "9781479941650",

pages = "266--269",

booktitle = "2014 International Conference on Electrical and Computer Engineering (ICECE 2014)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "International Conference on Electrical and Computer Engineering (ICECE) ; Conference date: 20-12-2014 Through 22-12-2014",

}

Kourous, N, Iosifidis, A, Tefas, A, Nikolaidis, N & Pitas, I 2015, Video characterization based on activity clustering. in 2014 International Conference on Electrical and Computer Engineering (ICECE 2014): Proceedings of a meeting held 20-22 December 2014, Dhaka, Bangladesh. Institute of Electrical and Electronics Engineers (IEEE), pp. 266-269, International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, 20/12/14. https://doi.org/10.1109/ICECE.2014.7026876

Video characterization based on activity clustering. / Kourous, Nikolaos; Iosifidis, Alexandros; Tefas, Anastasios et al.
2014 International Conference on Electrical and Computer Engineering (ICECE 2014): Proceedings of a meeting held 20-22 December 2014, Dhaka, Bangladesh. Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 266-269.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Video characterization based on activity clustering

AU - Kourous, Nikolaos

AU - Iosifidis, Alexandros

AU - Tefas, Anastasios

AU - Nikolaidis, Nikos

AU - Pitas, Ioannis

PY - 2015/3

Y1 - 2015/3

N2 - In this paper, we propose an efficient method for video characterization based on activity information. We employ a state-of-the-art video representation in order to learn human activity concepts, i.e., video groups formed by videos depictingsimilar human activities. In order to exploit the enriched visual information that is available in multi-view settings, we propose the use of the circular shift invariance property of the coefficients of the Discrete Fourier Transform (DFT) that leads to a view-independent multi-view action representation. In the test phase, in order to assign a test video to one (or multiple) activity groups, we perform temporal video segmentation in order to determine shorter videos depicting simple actions. Experimental results on the i3DPost multi-view action database and a new multi-view action database denote the effectiveness of the proposed approach.

AB - In this paper, we propose an efficient method for video characterization based on activity information. We employ a state-of-the-art video representation in order to learn human activity concepts, i.e., video groups formed by videos depictingsimilar human activities. In order to exploit the enriched visual information that is available in multi-view settings, we propose the use of the circular shift invariance property of the coefficients of the Discrete Fourier Transform (DFT) that leads to a view-independent multi-view action representation. In the test phase, in order to assign a test video to one (or multiple) activity groups, we perform temporal video segmentation in order to determine shorter videos depicting simple actions. Experimental results on the i3DPost multi-view action database and a new multi-view action database denote the effectiveness of the proposed approach.

KW - Video characterization

KW - Activity clustering

KW - Multi-camera setup

U2 - 10.1109/ICECE.2014.7026876

DO - 10.1109/ICECE.2014.7026876

M3 - Conference Contribution (Conference Proceeding)

SN - 9781479941650

SP - 266

EP - 269

BT - 2014 International Conference on Electrical and Computer Engineering (ICECE 2014)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - International Conference on Electrical and Computer Engineering (ICECE)

Y2 - 20 December 2014 through 22 December 2014

ER -

Kourous N, Iosifidis A, Tefas A, Nikolaidis N, Pitas I. Video characterization based on activity clustering. In 2014 International Conference on Electrical and Computer Engineering (ICECE 2014): Proceedings of a meeting held 20-22 December 2014, Dhaka, Bangladesh. Institute of Electrical and Electronics Engineers (IEEE). 2015. p. 266-269 Epub 2015 Jan 29. doi: 10.1109/ICECE.2014.7026876

Video characterization based on activity clustering

Abstract

Conference

Keywords

Access to Document

Handle.net

Fingerprint

Cite this