Improving robustness and precision in GEI + HOG action recognition

Tenika Whytock; Alexander Belyaev; Neil Robertson

doi:10.1007/978-3-642-41914-0_13

Improving robustness and precision in GEI + HOG action recognition

Tenika Whytock, Alexander Belyaev, Neil Robertson

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

4 Citations (Scopus)

Abstract

Histograms of Oriented Gradients is a well known and applied descriptor, however “black box” use is common. Gradient computation is the key to performance and may be application dependent. In this paper we examine explicit, implicit and Hessian schemes as opposed to the recommended centred mask. Results indicate the explicit Bickley scheme boosts robustness, both static and dynamic information are important to recognition and full body Gait-Energy Images are preferred. Robustness is boosted by specific choice of cell and bin parameters and SVM where actions are pre-classified using temporal information.

Original language	English
Title of host publication	Advances in Visual Computing
Subtitle of host publication	9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I
Editors	George Bebis , Richard Boyle, Bahram Parvin, Darko Koracin, Baoxin Li, Fatih Porikli, Victor Zordan , James Klosowski , Sabine Coquillart, Xun Luo , Min Chen , David Gotz
Publisher	Springer
Pages	119-128
Number of pages	10
ISBN (Electronic)	978-3-642-41914-0
ISBN (Print)	978-3-642-41913-3
DOIs	https://doi.org/10.1007/978-3-642-41914-0_13
Publication status	Published - 2013

Publication series

Name	Lecture Notes in Computer Science
Volume	8033
ISSN (Electronic)	0302-9743

Access to Document

10.1007/978-3-642-41914-0_13

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Whytock, T., Belyaev, A., & Robertson, N. (2013). Improving robustness and precision in GEI + HOG action recognition. In G. Bebis , R. Boyle, B. Parvin, D. Koracin, B. Li, F. Porikli, V. Zordan , J. Klosowski , S. Coquillart, X. Luo , M. Chen , & D. Gotz (Eds.), Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I (pp. 119-128). (Lecture Notes in Computer Science; Vol. 8033). Springer. https://doi.org/10.1007/978-3-642-41914-0_13

Whytock, Tenika ; Belyaev, Alexander ; Robertson, Neil. / Improving robustness and precision in GEI + HOG action recognition. Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I. editor / George Bebis ; Richard Boyle ; Bahram Parvin ; Darko Koracin ; Baoxin Li ; Fatih Porikli ; Victor Zordan ; James Klosowski ; Sabine Coquillart ; Xun Luo ; Min Chen ; David Gotz. Springer, 2013. pp. 119-128 (Lecture Notes in Computer Science).

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title = "Improving robustness and precision in GEI + HOG action recognition",

abstract = "Histograms of Oriented Gradients is a well known and applied descriptor, however “black box” use is common. Gradient computation is the key to performance and may be application dependent. In this paper we examine explicit, implicit and Hessian schemes as opposed to the recommended centred mask. Results indicate the explicit Bickley scheme boosts robustness, both static and dynamic information are important to recognition and full body Gait-Energy Images are preferred. Robustness is boosted by specific choice of cell and bin parameters and SVM where actions are pre-classified using temporal information.",

author = "Tenika Whytock and Alexander Belyaev and Neil Robertson",

year = "2013",

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language = "English",

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series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "119--128",

editor = "{Bebis }, {George } and Richard Boyle and Parvin, {Bahram } and Koracin, {Darko } and Baoxin Li and Porikli, {Fatih } and {Zordan }, {Victor } and {Klosowski }, {James } and Coquillart, {Sabine } and {Luo }, {Xun } and {Chen }, {Min } and Gotz, {David }",

booktitle = "Advances in Visual Computing",

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Whytock, T, Belyaev, A & Robertson, N 2013, Improving robustness and precision in GEI + HOG action recognition. in G Bebis , R Boyle, B Parvin, D Koracin, B Li, F Porikli, V Zordan , J Klosowski , S Coquillart, X Luo , M Chen & D Gotz (eds), Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I. Lecture Notes in Computer Science, vol. 8033, Springer, pp. 119-128. https://doi.org/10.1007/978-3-642-41914-0_13

Improving robustness and precision in GEI + HOG action recognition. / Whytock, Tenika; Belyaev, Alexander; Robertson, Neil.
Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I. ed. / George Bebis ; Richard Boyle; Bahram Parvin; Darko Koracin; Baoxin Li; Fatih Porikli; Victor Zordan ; James Klosowski ; Sabine Coquillart; Xun Luo ; Min Chen ; David Gotz. Springer, 2013. p. 119-128 (Lecture Notes in Computer Science; Vol. 8033).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

TY - CHAP

T1 - Improving robustness and precision in GEI + HOG action recognition

AU - Whytock, Tenika

AU - Belyaev, Alexander

AU - Robertson, Neil

PY - 2013

Y1 - 2013

N2 - Histograms of Oriented Gradients is a well known and applied descriptor, however “black box” use is common. Gradient computation is the key to performance and may be application dependent. In this paper we examine explicit, implicit and Hessian schemes as opposed to the recommended centred mask. Results indicate the explicit Bickley scheme boosts robustness, both static and dynamic information are important to recognition and full body Gait-Energy Images are preferred. Robustness is boosted by specific choice of cell and bin parameters and SVM where actions are pre-classified using temporal information.

AB - Histograms of Oriented Gradients is a well known and applied descriptor, however “black box” use is common. Gradient computation is the key to performance and may be application dependent. In this paper we examine explicit, implicit and Hessian schemes as opposed to the recommended centred mask. Results indicate the explicit Bickley scheme boosts robustness, both static and dynamic information are important to recognition and full body Gait-Energy Images are preferred. Robustness is boosted by specific choice of cell and bin parameters and SVM where actions are pre-classified using temporal information.

U2 - 10.1007/978-3-642-41914-0_13

DO - 10.1007/978-3-642-41914-0_13

M3 - Chapter (peer-reviewed)

SN - 978-3-642-41913-3

T3 - Lecture Notes in Computer Science

SP - 119

EP - 128

BT - Advances in Visual Computing

A2 - Bebis , George

A2 - Boyle, Richard

A2 - Parvin, Bahram

A2 - Koracin, Darko

A2 - Li, Baoxin

A2 - Porikli, Fatih

A2 - Zordan , Victor

A2 - Klosowski , James

A2 - Coquillart, Sabine

A2 - Luo , Xun

A2 - Chen , Min

A2 - Gotz, David

PB - Springer

ER -

Whytock T, Belyaev A, Robertson N. Improving robustness and precision in GEI + HOG action recognition. In Bebis G, Boyle R, Parvin B, Koracin D, Li B, Porikli F, Zordan V, Klosowski J, Coquillart S, Luo X, Chen M, Gotz D, editors, Advances in Visual Computing: 9th International Symposium, ISVC 2013, Rethymnon, Crete, Greece, July 29-31, 2013. Proceedings, Part I. Springer. 2013. p. 119-128. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-41914-0_13

Improving robustness and precision in GEI + HOG action recognition

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