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 avons proposé une méthode pour détecter et extraire quantitativement les anomalies des vidéos de surveillance. Grâce à notre méthode, les anomalies sont détectées sous forme de modèles basés sur des caractéristiques spatio-temporelles qui sont des valeurs aberrantes dans le nouvel espace de fonctionnalités. Les méthodes conventionnelles de détection d’anomalies utilisent des caractéristiques telles que des traces ou des caractéristiques spatio-temporelles locales, qui fournissent toutes deux des informations temporelles insuffisantes. Grâce à notre méthode, les principales composantes des caractéristiques spatio-temporelles du changement sont extraites des images de séquences vidéo d'une durée de plusieurs secondes. Cela permet de déterminer des anomalies basées sur une irrégularité de mouvement, à la fois de position et de vitesse, et permet ainsi la détection automatique d'événements anormaux dans des séquences de longueur constante sans tenir compte de leur début et de leur fin. Nous avons utilisé un SVM de classe 1, qui est une méthode de détection des valeurs aberrantes non supervisée. La sortie du SVM indique la distance entre la valeur aberrante et le modèle de base concentré. Nous avons démontré que les anomalies extraites à l’aide de notre méthode correspondaient subjectivement aux irrégularités perçues dans la configuration des mouvements. Notre méthode est utile dans les services de surveillance car les images capturées peuvent être affichées par ordre d'anomalie, ce qui réduit considérablement le temps nécessaire.
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Kyoko SUDO, Tatsuya OSAWA, Kaoru WAKABAYASHI, Hideki KOIKE, Kenichi ARAKAWA, "Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 7, pp. 1929-1936, July 2008, doi: 10.1093/ietisy/e91-d.7.1929.
Abstract: We have proposed a method to detect and quantitatively extract anomalies from surveillance videos. Using our method, anomalies are detected as patterns based on spatio-temporal features that are outliers in new feature space. Conventional anomaly detection methods use features such as tracks or local spatio-temporal features, both of which provide insufficient timing information. Using our method, the principal components of spatio-temporal features of change are extracted from the frames of video sequences of several seconds duration. This enables anomalies based on movement irregularity, both position and speed, to be determined and thus permits the automatic detection of anomal events in sequences of constant length without regard to their start and end. We used a 1-class SVM, which is a non-supervised outlier detection method. The output from the SVM indicates the distance between the outlier and the concentrated base pattern. We demonstrated that the anomalies extracted using our method subjectively matched perceived irregularities in the pattern of movements. Our method is useful in surveillance services because the captured images can be shown in the order of anomality, which significantly reduces the time needed.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.7.1929/_p
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@ARTICLE{e91-d_7_1929,
author={Kyoko SUDO, Tatsuya OSAWA, Kaoru WAKABAYASHI, Hideki KOIKE, Kenichi ARAKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM},
year={2008},
volume={E91-D},
number={7},
pages={1929-1936},
abstract={We have proposed a method to detect and quantitatively extract anomalies from surveillance videos. Using our method, anomalies are detected as patterns based on spatio-temporal features that are outliers in new feature space. Conventional anomaly detection methods use features such as tracks or local spatio-temporal features, both of which provide insufficient timing information. Using our method, the principal components of spatio-temporal features of change are extracted from the frames of video sequences of several seconds duration. This enables anomalies based on movement irregularity, both position and speed, to be determined and thus permits the automatic detection of anomal events in sequences of constant length without regard to their start and end. We used a 1-class SVM, which is a non-supervised outlier detection method. The output from the SVM indicates the distance between the outlier and the concentrated base pattern. We demonstrated that the anomalies extracted using our method subjectively matched perceived irregularities in the pattern of movements. Our method is useful in surveillance services because the captured images can be shown in the order of anomality, which significantly reduces the time needed.},
keywords={},
doi={10.1093/ietisy/e91-d.7.1929},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM
T2 - IEICE TRANSACTIONS on Information
SP - 1929
EP - 1936
AU - Kyoko SUDO
AU - Tatsuya OSAWA
AU - Kaoru WAKABAYASHI
AU - Hideki KOIKE
AU - Kenichi ARAKAWA
PY - 2008
DO - 10.1093/ietisy/e91-d.7.1929
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2008
AB - We have proposed a method to detect and quantitatively extract anomalies from surveillance videos. Using our method, anomalies are detected as patterns based on spatio-temporal features that are outliers in new feature space. Conventional anomaly detection methods use features such as tracks or local spatio-temporal features, both of which provide insufficient timing information. Using our method, the principal components of spatio-temporal features of change are extracted from the frames of video sequences of several seconds duration. This enables anomalies based on movement irregularity, both position and speed, to be determined and thus permits the automatic detection of anomal events in sequences of constant length without regard to their start and end. We used a 1-class SVM, which is a non-supervised outlier detection method. The output from the SVM indicates the distance between the outlier and the concentrated base pattern. We demonstrated that the anomalies extracted using our method subjectively matched perceived irregularities in the pattern of movements. Our method is useful in surveillance services because the captured images can be shown in the order of anomality, which significantly reduces the time needed.
ER -