An SVM approach for activity recognition based on chord-length-function shape features

Samy Sadek; Ayoub Al-Hamadi; Bernd Michaelis; Usama Sayed

doi:10.1109/ICIP.2012.6466972

An SVM approach for activity recognition based on chord-length-function shape features

Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed

Otto von Guericke University Magdeburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Despite their high stability and compactness, chord-length features have received little attention in activity recognition literature. In this paper, we present an SVM approach for activity recognition, based on chord-length shape features. The main contribution of the paper is two-fold. We first show how a compact computationally-efficient shape descriptor is constructed using 1-D chord-length functions. Secondly, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. When tested on KTH benchmark dataset, the approach achieves promising results that compare very favorably with those reported in the literature, while maintaining real-time performance.

Original language	English
Title of host publication	2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages	765-768
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2012.6466972
State	Published - 2012
Externally published	Yes
Event	2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States Duration: 30 Sep 2012 → 3 Oct 2012

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2012 19th IEEE International Conference on Image Processing, ICIP 2012
Country/Territory	United States
City	Lake Buena Vista, FL
Period	30/09/12 → 3/10/12

Keywords

chord-length function
Human action recognition
shape features
video interpretation

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10.1109/ICIP.2012.6466972

Cite this

Sadek, S., Al-Hamadi, A., Michaelis, B., & Sayed, U. (2012). An SVM approach for activity recognition based on chord-length-function shape features. In 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings (pp. 765-768). Article 6466972 (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2012.6466972

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title = "An SVM approach for activity recognition based on chord-length-function shape features",

abstract = "Despite their high stability and compactness, chord-length features have received little attention in activity recognition literature. In this paper, we present an SVM approach for activity recognition, based on chord-length shape features. The main contribution of the paper is two-fold. We first show how a compact computationally-efficient shape descriptor is constructed using 1-D chord-length functions. Secondly, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. When tested on KTH benchmark dataset, the approach achieves promising results that compare very favorably with those reported in the literature, while maintaining real-time performance.",

keywords = "chord-length function, Human action recognition, shape features, video interpretation",

author = "Samy Sadek and Ayoub Al-Hamadi and Bernd Michaelis and Usama Sayed",

year = "2012",

doi = "10.1109/ICIP.2012.6466972",

language = "English",

isbn = "9781467325332",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "765--768",

booktitle = "2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings",

note = "2012 19th IEEE International Conference on Image Processing, ICIP 2012 ; Conference date: 30-09-2012 Through 03-10-2012",

}

Sadek, S, Al-Hamadi, A, Michaelis, B & Sayed, U 2012, An SVM approach for activity recognition based on chord-length-function shape features. in 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings., 6466972, Proceedings - International Conference on Image Processing, ICIP, pp. 765-768, 2012 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, FL, United States, 30/09/12. https://doi.org/10.1109/ICIP.2012.6466972

An SVM approach for activity recognition based on chord-length-function shape features. / Sadek, Samy; Al-Hamadi, Ayoub; Michaelis, Bernd et al.
2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. p. 765-768 6466972 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Al-Hamadi, Ayoub

AU - Michaelis, Bernd

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N2 - Despite their high stability and compactness, chord-length features have received little attention in activity recognition literature. In this paper, we present an SVM approach for activity recognition, based on chord-length shape features. The main contribution of the paper is two-fold. We first show how a compact computationally-efficient shape descriptor is constructed using 1-D chord-length functions. Secondly, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. When tested on KTH benchmark dataset, the approach achieves promising results that compare very favorably with those reported in the literature, while maintaining real-time performance.

AB - Despite their high stability and compactness, chord-length features have received little attention in activity recognition literature. In this paper, we present an SVM approach for activity recognition, based on chord-length shape features. The main contribution of the paper is two-fold. We first show how a compact computationally-efficient shape descriptor is constructed using 1-D chord-length functions. Secondly, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. When tested on KTH benchmark dataset, the approach achieves promising results that compare very favorably with those reported in the literature, while maintaining real-time performance.

KW - chord-length function

KW - Human action recognition

KW - shape features

KW - video interpretation

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DO - 10.1109/ICIP.2012.6466972

M3 - Conference contribution

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SN - 9781467325332

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 765

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BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings

T2 - 2012 19th IEEE International Conference on Image Processing, ICIP 2012

Y2 - 30 September 2012 through 3 October 2012

ER -

An SVM approach for activity recognition based on chord-length-function shape features

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