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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

4 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013
Toimittajat	Joni-Kristian Kämäräinen, Markus Koskela
Julkaisupaikka	Espoo
Kustantaja	Springer Gabler
Sivut	361-372
ISBN (painettu)	978-3-642-38885-9
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-642-38886-6_35
Tila	Julkaistu - 2013
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	Scandinavian Conference on Image Analysis - Espoo, Suomi Kesto: 17 kesäk. 2013 → 20 kesäk. 2013 Konferenssinumero: 18

Julkaisusarja

Nimi	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Kustantaja	Springer
Vuosikerta	7944
ISSN (painettu)	0302-9743

Conference

Conference	Scandinavian Conference on Image Analysis
Lyhennettä	SCIA
Maa/Alue	Suomi
Kaupunki	Espoo
Ajanjakso	17/06/2013 → 20/06/2013

Pääsy asiakirjaan

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

Siteeraa tätä

Viitaniemi, V., Karppa, M., Laaksonen, J., & Jantunen, T. (2013). Detecting Hand-Head Occlusions in Sign Language Video. teoksessa J.-K. Kämäräinen, & M. Koskela (Toimittajat), 18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013 (Sivut 361-372). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 7944). Springer Gabler . 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. Toimittaja / Joni-Kristian Kämäräinen ; Markus Koskela. Espoo : Springer Gabler , 2013. Sivut 361-372 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{cfb1e18ba913405abe47201bc8ea5623,

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",

note = "VK: coin cbir sl; Scandinavian Conference on Image Analysis, SCIA ; Conference date: 17-06-2013 Through 20-06-2013",

year = "2013",

doi = "10.1007/978-3-642-38886-6_35",

language = "English",

isbn = "978-3-642-38885-9",

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

publisher = "Springer Gabler ",

pages = "361--372",

editor = "Joni-Kristian K{\"a}m{\"a}r{\"a}inen and Markus Koskela",

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

address = "Germany",

}

Viitaniemi, V, Karppa, M, Laaksonen, J & Jantunen, T 2013, Detecting Hand-Head Occlusions in Sign Language Video. julkaisussa J-K Kämäräinen & M Koskela (toim), 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), Vuosikerta 7944, Springer Gabler , Espoo, Sivut 361-372, Scandinavian Conference on Image Analysis, Espoo, Suomi, 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. toim. / Joni-Kristian Kämäräinen; Markus Koskela. Espoo: Springer Gabler , 2013. s. 361-372 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 7944).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

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 Gabler

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. julkaisussa Kämäräinen JK, Koskela M, toimittajat, 18th Scandinavian Conference on Image Analysis, (SCIA 2013), Espoo, Finland, 17-20 June 2013. Espoo: Springer Gabler . 2013. s. 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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