Gait-based person identification using motion interchange patterns

Gil Freidlin; Noga Levy; Lior Wolf

doi:10.1007/978-3-319-16181-5_7

Gait-based person identification using motion interchange patterns

Gil Freidlin^*, Noga Levy, Lior Wolf

^*Corresponding author for this work

School of Computer Science

Tel Aviv University

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

Abstract

Understanding human motion in unconstrained 2D videos has been a central theme in Computer Vision research, and over the years many attempts have been made to design effective representations of video content. In this paper, we apply to gait recognition the Motion Interchange Patterns (MIP) framework, a 3D extension of the LBP descriptors to videos that was successfully employed in action recognition. This effective framework encodes motion by capturing local changes in motion directions. Our scheme does not rely on silhouettes commonly used in gait recognition, and benefits from the capability of MIP encoding to model real world videos. We empirically demonstrate the effectiveness of this modeling of human motion on several challenging gait recognition datasets.

Original language	English
Title of host publication	Computer Vision - ECCV 2014 Workshops, Proceedings
Editors	Carsten Rother, Michael M. Bronstein, Lourdes Agapito
Publisher	Springer Verlag
Pages	94-110
Number of pages	17
ISBN (Electronic)	9783319161808
DOIs	https://doi.org/10.1007/978-3-319-16181-5_7
State	Published - 2015
Event	13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sep 2014 → 12 Sep 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8926
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th European Conference on Computer Vision, ECCV 2014
Country/Territory	Switzerland
City	Zurich
Period	6/09/14 → 12/09/14

Keywords

CASIA
Gait recognition
LBP
MIP
TUMGAID

Access to Document

10.1007/978-3-319-16181-5_7

Cite this

Freidlin, G., Levy, N., & Wolf, L. (2015). Gait-based person identification using motion interchange patterns. In C. Rother, M. M. Bronstein, & L. Agapito (Eds.), Computer Vision - ECCV 2014 Workshops, Proceedings (pp. 94-110). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8926). Springer Verlag. https://doi.org/10.1007/978-3-319-16181-5_7

Freidlin, Gil ; Levy, Noga ; Wolf, Lior. / Gait-based person identification using motion interchange patterns. Computer Vision - ECCV 2014 Workshops, Proceedings. editor / Carsten Rother ; Michael M. Bronstein ; Lourdes Agapito. Springer Verlag, 2015. pp. 94-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{cf1d6e118b8e40b8b92cbdc3d7186f79,

title = "Gait-based person identification using motion interchange patterns",

abstract = "Understanding human motion in unconstrained 2D videos has been a central theme in Computer Vision research, and over the years many attempts have been made to design effective representations of video content. In this paper, we apply to gait recognition the Motion Interchange Patterns (MIP) framework, a 3D extension of the LBP descriptors to videos that was successfully employed in action recognition. This effective framework encodes motion by capturing local changes in motion directions. Our scheme does not rely on silhouettes commonly used in gait recognition, and benefits from the capability of MIP encoding to model real world videos. We empirically demonstrate the effectiveness of this modeling of human motion on several challenging gait recognition datasets.",

keywords = "CASIA, Gait recognition, LBP, MIP, TUMGAID",

author = "Gil Freidlin and Noga Levy and Lior Wolf",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",

year = "2015",

doi = "10.1007/978-3-319-16181-5_7",

language = "אנגלית",

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

publisher = "Springer Verlag",

pages = "94--110",

editor = "Carsten Rother and Bronstein, {Michael M.} and Lourdes Agapito",

booktitle = "Computer Vision - ECCV 2014 Workshops, Proceedings",

}

Freidlin, G, Levy, N & Wolf, L 2015, Gait-based person identification using motion interchange patterns. in C Rother, MM Bronstein & L Agapito (eds), Computer Vision - ECCV 2014 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8926, Springer Verlag, pp. 94-110, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 6/09/14. https://doi.org/10.1007/978-3-319-16181-5_7

Gait-based person identification using motion interchange patterns. / Freidlin, Gil; Levy, Noga; Wolf, Lior.
Computer Vision - ECCV 2014 Workshops, Proceedings. ed. / Carsten Rother; Michael M. Bronstein; Lourdes Agapito. Springer Verlag, 2015. p. 94-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8926).

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

TY - GEN

T1 - Gait-based person identification using motion interchange patterns

AU - Freidlin, Gil

AU - Levy, Noga

AU - Wolf, Lior

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Understanding human motion in unconstrained 2D videos has been a central theme in Computer Vision research, and over the years many attempts have been made to design effective representations of video content. In this paper, we apply to gait recognition the Motion Interchange Patterns (MIP) framework, a 3D extension of the LBP descriptors to videos that was successfully employed in action recognition. This effective framework encodes motion by capturing local changes in motion directions. Our scheme does not rely on silhouettes commonly used in gait recognition, and benefits from the capability of MIP encoding to model real world videos. We empirically demonstrate the effectiveness of this modeling of human motion on several challenging gait recognition datasets.

AB - Understanding human motion in unconstrained 2D videos has been a central theme in Computer Vision research, and over the years many attempts have been made to design effective representations of video content. In this paper, we apply to gait recognition the Motion Interchange Patterns (MIP) framework, a 3D extension of the LBP descriptors to videos that was successfully employed in action recognition. This effective framework encodes motion by capturing local changes in motion directions. Our scheme does not rely on silhouettes commonly used in gait recognition, and benefits from the capability of MIP encoding to model real world videos. We empirically demonstrate the effectiveness of this modeling of human motion on several challenging gait recognition datasets.

KW - CASIA

KW - Gait recognition

KW - LBP

KW - MIP

KW - TUMGAID

UR - http://www.scopus.com/inward/record.url?scp=84928800209&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-16181-5_7

DO - 10.1007/978-3-319-16181-5_7

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:84928800209

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 94

EP - 110

BT - Computer Vision - ECCV 2014 Workshops, Proceedings

A2 - Rother, Carsten

A2 - Bronstein, Michael M.

A2 - Agapito, Lourdes

PB - Springer Verlag

T2 - 13th European Conference on Computer Vision, ECCV 2014

Y2 - 6 September 2014 through 12 September 2014

ER -

Freidlin G, Levy N, Wolf L. Gait-based person identification using motion interchange patterns. In Rother C, Bronstein MM, Agapito L, editors, Computer Vision - ECCV 2014 Workshops, Proceedings. Springer Verlag. 2015. p. 94-110. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-16181-5_7

Gait-based person identification using motion interchange patterns

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this