The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous proposons une méthode pour identifier avec précision les personnes en utilisant les changements temporels et spatiaux des mouvements locaux mesurés à partir de séquences vidéo de balancement du corps. Les méthodes existantes identifient les personnes utilisant des caractéristiques de démarche qui représentent principalement le grand balancement des membres. L'utilisation des caractéristiques de la démarche introduit un problème dans la mesure où les performances d'identification diminuent lorsque les personnes arrêtent de marcher et maintiennent une posture droite. Pour extraire des caractéristiques informatives, notre méthode mesure les petites oscillations du corps, appelées balancement du corps. Nous extrayons la densité spectrale de puissance en tant que caractéristique des mouvements de balancement locaux du corps en divisant le corps en régions. Pour évaluer les performances d'identification à l'aide de notre méthode, nous avons collecté trois ensembles de données vidéo originales de séquences de balancement corporel. Le premier ensemble de données contenait un grand nombre de participants en position verticale. Le deuxième ensemble de données comprenait une variation sur le long terme. Le troisième ensemble de données représentait le balancement du corps dans différentes postures. Les résultats sur les ensembles de données ont confirmé que notre méthode utilisant les mouvements locaux mesurés à partir du balancement du corps peut extraire des caractéristiques informatives pour l'identification.
Takuya KAMITANI
Tottori University
Hiroki YOSHIMURA
Tottori University
Masashi NISHIYAMA
Tottori University
Yoshio IWAI
Tottori University
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Takuya KAMITANI, Hiroki YOSHIMURA, Masashi NISHIYAMA, Yoshio IWAI, "Temporal and Spatial Analysis of Local Body Sway Movements for the Identification of People" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 1, pp. 165-174, January 2019, doi: 10.1587/transinf.2018EDP7182.
Abstract: We propose a method for accurately identifying people using temporal and spatial changes in local movements measured from video sequences of body sway. Existing methods identify people using gait features that mainly represent the large swinging of the limbs. The use of gait features introduces a problem in that the identification performance decreases when people stop walking and maintain an upright posture. To extract informative features, our method measures small swings of the body, referred to as body sway. We extract the power spectral density as a feature from local body sway movements by dividing the body into regions. To evaluate the identification performance using our method, we collected three original video datasets of body sway sequences. The first dataset contained a large number of participants in an upright posture. The second dataset included variation over the long term. The third dataset represented body sway in different postures. The results on the datasets confirmed that our method using local movements measured from body sway can extract informative features for identification.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7182/_p
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@ARTICLE{e102-d_1_165,
author={Takuya KAMITANI, Hiroki YOSHIMURA, Masashi NISHIYAMA, Yoshio IWAI, },
journal={IEICE TRANSACTIONS on Information},
title={Temporal and Spatial Analysis of Local Body Sway Movements for the Identification of People},
year={2019},
volume={E102-D},
number={1},
pages={165-174},
abstract={We propose a method for accurately identifying people using temporal and spatial changes in local movements measured from video sequences of body sway. Existing methods identify people using gait features that mainly represent the large swinging of the limbs. The use of gait features introduces a problem in that the identification performance decreases when people stop walking and maintain an upright posture. To extract informative features, our method measures small swings of the body, referred to as body sway. We extract the power spectral density as a feature from local body sway movements by dividing the body into regions. To evaluate the identification performance using our method, we collected three original video datasets of body sway sequences. The first dataset contained a large number of participants in an upright posture. The second dataset included variation over the long term. The third dataset represented body sway in different postures. The results on the datasets confirmed that our method using local movements measured from body sway can extract informative features for identification.},
keywords={},
doi={10.1587/transinf.2018EDP7182},
ISSN={1745-1361},
month={January},}
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TY - JOUR
TI - Temporal and Spatial Analysis of Local Body Sway Movements for the Identification of People
T2 - IEICE TRANSACTIONS on Information
SP - 165
EP - 174
AU - Takuya KAMITANI
AU - Hiroki YOSHIMURA
AU - Masashi NISHIYAMA
AU - Yoshio IWAI
PY - 2019
DO - 10.1587/transinf.2018EDP7182
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2019
AB - We propose a method for accurately identifying people using temporal and spatial changes in local movements measured from video sequences of body sway. Existing methods identify people using gait features that mainly represent the large swinging of the limbs. The use of gait features introduces a problem in that the identification performance decreases when people stop walking and maintain an upright posture. To extract informative features, our method measures small swings of the body, referred to as body sway. We extract the power spectral density as a feature from local body sway movements by dividing the body into regions. To evaluate the identification performance using our method, we collected three original video datasets of body sway sequences. The first dataset contained a large number of participants in an upright posture. The second dataset included variation over the long term. The third dataset represented body sway in different postures. The results on the datasets confirmed that our method using local movements measured from body sway can extract informative features for identification.
ER -