Analysis of temporal coherence in videos for action recognition

Adel Saleh; Mohamed Abdel-Nasser; Farhan Akram; Miguel Angel Garcia; Domenec Puig

doi:10.1007/978-3-319-41501-7_37

Analysis of temporal coherence in videos for action recognition

Adel Saleh^*, Mohamed Abdel-Nasser, Farhan Akram, Miguel Angel Garcia, Domenec Puig

^*Corresponding author for this work

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

Abstract

This paper proposes an approach to improve the performance of activity recognition methods by analyzing the coherence of the frames in the input videos and then modeling the evolution of the coherent frames, which constitute a sub-sequence, to learn a representation for the videos. The proposed method consist of three steps: coherence analysis, representation leaning and classification. Using two state-of-the-art datasets (Hollywood2 and HMDB51), we demonstrate that learning the evolution of subsequences in lieu of frames, improves the recognition results and makes actions classification faster.

Original language	English
Title of host publication	Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings
Editors	Aurelio Campilho, Aurelio Campilho, Fakhri Karray
Pages	325-332
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-319-41501-7_37
Publication status	Published - 2016
Externally published	Yes
Event	13th International Conference on Image Analysis and Recognition, ICIAR 2016 - Povoa de Varzim, Portugal Duration: 13 Jul 2016 → 16 Jul 2016

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9730
ISSN	0302-9743

Conference

Conference	13th International Conference on Image Analysis and Recognition, ICIAR 2016
Country/Territory	Portugal
City	Povoa de Varzim
Period	13/07/16 → 16/07/16

Bibliographical note

Funding Information:
This work was partly supported by Universitat Rovira i Virgili, Spain, and Hodeidah University, Yemen.

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Access to Document

10.1007/978-3-319-41501-7_37

Cite this

Saleh, Adel ; Abdel-Nasser, Mohamed ; Akram, Farhan et al. / Analysis of temporal coherence in videos for action recognition. Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. editor / Aurelio Campilho ; Aurelio Campilho ; Fakhri Karray. 2016. pp. 325-332 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9730).

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title = "Analysis of temporal coherence in videos for action recognition",

abstract = "This paper proposes an approach to improve the performance of activity recognition methods by analyzing the coherence of the frames in the input videos and then modeling the evolution of the coherent frames, which constitute a sub-sequence, to learn a representation for the videos. The proposed method consist of three steps: coherence analysis, representation leaning and classification. Using two state-of-the-art datasets (Hollywood2 and HMDB51), we demonstrate that learning the evolution of subsequences in lieu of frames, improves the recognition results and makes actions classification faster.",

author = "Adel Saleh and Mohamed Abdel-Nasser and Farhan Akram and Garcia, \{Miguel Angel\} and Domenec Puig",

note = "Funding Information: This work was partly supported by Universitat Rovira i Virgili, Spain, and Hodeidah University, Yemen. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 13th International Conference on Image Analysis and Recognition, ICIAR 2016 ; Conference date: 13-07-2016 Through 16-07-2016",

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doi = "10.1007/978-3-319-41501-7\_37",

language = "English",

isbn = "9783319415000",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Aurelio Campilho and Aurelio Campilho and Fakhri Karray",

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Saleh, A, Abdel-Nasser, M, Akram, F, Garcia, MA & Puig, D 2016, Analysis of temporal coherence in videos for action recognition. in A Campilho, A Campilho & F Karray (eds), Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9730, pp. 325-332, 13th International Conference on Image Analysis and Recognition, ICIAR 2016, Povoa de Varzim, Portugal, 13/07/16. https://doi.org/10.1007/978-3-319-41501-7_37

Analysis of temporal coherence in videos for action recognition. / Saleh, Adel; Abdel-Nasser, Mohamed; Akram, Farhan et al.
Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. ed. / Aurelio Campilho; Aurelio Campilho; Fakhri Karray. 2016. p. 325-332 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9730).

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

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AU - Abdel-Nasser, Mohamed

AU - Akram, Farhan

AU - Garcia, Miguel Angel

AU - Puig, Domenec

N1 - Funding Information: This work was partly supported by Universitat Rovira i Virgili, Spain, and Hodeidah University, Yemen. Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - This paper proposes an approach to improve the performance of activity recognition methods by analyzing the coherence of the frames in the input videos and then modeling the evolution of the coherent frames, which constitute a sub-sequence, to learn a representation for the videos. The proposed method consist of three steps: coherence analysis, representation leaning and classification. Using two state-of-the-art datasets (Hollywood2 and HMDB51), we demonstrate that learning the evolution of subsequences in lieu of frames, improves the recognition results and makes actions classification faster.

AB - This paper proposes an approach to improve the performance of activity recognition methods by analyzing the coherence of the frames in the input videos and then modeling the evolution of the coherent frames, which constitute a sub-sequence, to learn a representation for the videos. The proposed method consist of three steps: coherence analysis, representation leaning and classification. Using two state-of-the-art datasets (Hollywood2 and HMDB51), we demonstrate that learning the evolution of subsequences in lieu of frames, improves the recognition results and makes actions classification faster.

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BT - Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings

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Saleh A, Abdel-Nasser M, Akram F, Garcia MA, Puig D. Analysis of temporal coherence in videos for action recognition. In Campilho A, Campilho A, Karray F, editors, Image Analysis and Recognition - 13th International Conference, ICIAR 2016, Proceedings. 2016. p. 325-332. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9730). doi: 10.1007/978-3-319-41501-7_37

Analysis of temporal coherence in videos for action recognition

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