Detecting Hand-Head Occlusions in Sign Language Video

Ville Viitaniemi; Matti Karppa; Jorma Laaksonen; Tommi Jantunen

doi:10.1007/978-3-642-38886-6_35

Detecting Hand-Head Occlusions in Sign Language Video

Ville Viitaniemi, Matti Karppa, Jorma Laaksonen, Tommi Jantunen

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

Abstract

A large body of current linguistic research on sign language is based on analyzing large corpora of video recordings. This requires either manual or automatic annotation of the videos. In this paper we introduce methods for automatically detecting and classifying hand-head occlusions in sign language videos. Linguistically, hand-head occlusions are an important and interesting subject of study as the head is a structural place of articulation in many signs. Our method combines easily calculable local video properties with more global hand tracking. The experiments carried out with videos of the Suvi on-line dictionary of Finnish Sign Language show that the sensitivity of the proposed local method in detecting occlusion events is 92.6%. When global hand tracking is combined in the method, the specificity can reach the level of 93.7% while still maintaining the detection sensitivity above 90%. © 2013 Springer-Verlag.

Original language	English
Title of host publication	18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013
Editors	Joni-Kristian Kämäräinen, Markus Koskela
Place of Publication	Espoo
Publisher	Springer
Pages	361-372
ISBN (Print)	978-3-642-38885-9
DOIs	https://doi.org/10.1007/978-3-642-38886-6_35
Publication status	Published - 2013
MoE publication type	A4 Conference publication
Event	Scandinavian Conference on Image Analysis - Espoo, Finland Duration: 17 Jun 2013 → 20 Jun 2013 Conference number: 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	7944
ISSN (Print)	0302-9743

Conference

Conference	Scandinavian Conference on Image Analysis
Abbreviated title	SCIA
Country/Territory	Finland
City	Espoo
Period	17/06/2013 → 20/06/2013

Keywords

Extreme Learning Machine
Sign Language
Sign Language Video
Head Region
Facial Landmark

Access to Document

10.1007/978-3-642-38886-6_35

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Viitaniemi, V., Karppa, M., Laaksonen, J., & Jantunen, T. (2013). Detecting Hand-Head Occlusions in Sign Language Video. In J.-K. Kämäräinen, & M. Koskela (Eds.), 18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013 (pp. 361-372). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7944). Springer. https://doi.org/10.1007/978-3-642-38886-6_35

Viitaniemi, Ville ; Karppa, Matti ; Laaksonen, Jorma et al. / Detecting Hand-Head Occlusions in Sign Language Video. 18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013. editor / Joni-Kristian Kämäräinen ; Markus Koskela. Espoo : Springer, 2013. pp. 361-372 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Detecting Hand-Head Occlusions in Sign Language Video",

abstract = "A large body of current linguistic research on sign language is based on analyzing large corpora of video recordings. This requires either manual or automatic annotation of the videos. In this paper we introduce methods for automatically detecting and classifying hand-head occlusions in sign language videos. Linguistically, hand-head occlusions are an important and interesting subject of study as the head is a structural place of articulation in many signs. Our method combines easily calculable local video properties with more global hand tracking. The experiments carried out with videos of the Suvi on-line dictionary of Finnish Sign Language show that the sensitivity of the proposed local method in detecting occlusion events is 92.6%. When global hand tracking is combined in the method, the specificity can reach the level of 93.7% while still maintaining the detection sensitivity above 90%. {\textcopyright} 2013 Springer-Verlag.",

keywords = "Extreme Learning Machine, Sign Language, Sign Language Video, Head Region, Facial Landmark",

author = "Ville Viitaniemi and Matti Karppa and Jorma Laaksonen and Tommi Jantunen",

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Viitaniemi, V, Karppa, M, Laaksonen, J & Jantunen, T 2013, Detecting Hand-Head Occlusions in Sign Language Video. in J-K Kämäräinen & M Koskela (eds), 18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7944, Springer, Espoo, pp. 361-372, Scandinavian Conference on Image Analysis, Espoo, Finland, 17/06/2013. https://doi.org/10.1007/978-3-642-38886-6_35

Detecting Hand-Head Occlusions in Sign Language Video. / Viitaniemi, Ville; Karppa, Matti; Laaksonen, Jorma et al.
18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013. ed. / Joni-Kristian Kämäräinen; Markus Koskela. Espoo: Springer, 2013. p. 361-372 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7944).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Detecting Hand-Head Occlusions in Sign Language Video

AU - Viitaniemi, Ville

AU - Karppa, Matti

AU - Laaksonen, Jorma

AU - Jantunen, Tommi

N1 - Conference code: 18

PY - 2013

Y1 - 2013

N2 - A large body of current linguistic research on sign language is based on analyzing large corpora of video recordings. This requires either manual or automatic annotation of the videos. In this paper we introduce methods for automatically detecting and classifying hand-head occlusions in sign language videos. Linguistically, hand-head occlusions are an important and interesting subject of study as the head is a structural place of articulation in many signs. Our method combines easily calculable local video properties with more global hand tracking. The experiments carried out with videos of the Suvi on-line dictionary of Finnish Sign Language show that the sensitivity of the proposed local method in detecting occlusion events is 92.6%. When global hand tracking is combined in the method, the specificity can reach the level of 93.7% while still maintaining the detection sensitivity above 90%. © 2013 Springer-Verlag.

AB - A large body of current linguistic research on sign language is based on analyzing large corpora of video recordings. This requires either manual or automatic annotation of the videos. In this paper we introduce methods for automatically detecting and classifying hand-head occlusions in sign language videos. Linguistically, hand-head occlusions are an important and interesting subject of study as the head is a structural place of articulation in many signs. Our method combines easily calculable local video properties with more global hand tracking. The experiments carried out with videos of the Suvi on-line dictionary of Finnish Sign Language show that the sensitivity of the proposed local method in detecting occlusion events is 92.6%. When global hand tracking is combined in the method, the specificity can reach the level of 93.7% while still maintaining the detection sensitivity above 90%. © 2013 Springer-Verlag.

KW - Extreme Learning Machine

KW - Sign Language

KW - Sign Language Video

KW - Head Region

KW - Facial Landmark

U2 - 10.1007/978-3-642-38886-6_35

DO - 10.1007/978-3-642-38886-6_35

M3 - Conference article in proceedings

SN - 978-3-642-38885-9

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

SP - 361

EP - 372

BT - 18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013

A2 - Kämäräinen, Joni-Kristian

A2 - Koskela, Markus

PB - Springer

CY - Espoo

T2 - Scandinavian Conference on Image Analysis

Y2 - 17 June 2013 through 20 June 2013

ER -

Viitaniemi V, Karppa M, Laaksonen J, Jantunen T. Detecting Hand-Head Occlusions in Sign Language Video. In Kämäräinen JK, Koskela M, editors, 18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013. Espoo: Springer. 2013. p. 361-372. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-38886-6_35

Detecting Hand-Head Occlusions in Sign Language Video

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Keywords

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