Sikistmlrrus videoda anomali tespiti

Sumeyye Cavas; Muhammet Sebul Beratoglu; Behcet Ugur Toreyin

doi:10.1109/SIU53274.2021.9478048

Sikistmlrrus videoda anomali tespiti

Bilişim Enstitüsü

Istanbul Technical University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

1 Atıf (Scopus)

Özet

In this paper, an anomaly detection approach has been developed on video compressed in H.265 format. In order to detect anomalies, the motion vectors in the compressed video and the region information of the motion vectors were used. This information was provided as input to the autoencoder model, which is an unsupervised artificial neural network method, and thus the model was trained. The trained model was tested on video data containing anomalies. As output, during the streaming of any video, it is provided to draw a regularity score graph and display the anomaly regions by color. In this paper, we propose an autoencoder based method for anomaly detection in compressed video instead of the original uncompressed video.

Tercüme edilen katkı başlığı	Anomaly detection in compressed video
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781665436496
DOI'lar	https://doi.org/10.1109/SIU53274.2021.9478048
Yayın durumu	Yayınlandı - 9 Haz 2021
Etkinlik	29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 - Virtual, Istanbul, Türkiye Süre: 9 Haz 2021 → 11 Haz 2021

Yayın serisi

Adı	SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021
Ülke/Bölge	Türkiye
Şehir	Virtual, Istanbul
Periyot	9/06/21 → 11/06/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

Keywords

Anomaly detection
Compressed domain video analysis
Compressed video
Motion vector

Belgeye Erişim

10.1109/SIU53274.2021.9478048

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Cavas, S., Beratoglu, M. S., & Toreyin, B. U. (2021). Sikistmlrrus videoda anomali tespiti. In SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings Makale 9478048 (SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU53274.2021.9478048

Cavas, Sumeyye ; Beratoglu, Muhammet Sebul ; Toreyin, Behcet Ugur. / Sikistmlrrus videoda anomali tespiti. SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings).

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title = "Sikistmlrrus videoda anomali tespiti",

abstract = "In this paper, an anomaly detection approach has been developed on video compressed in H.265 format. In order to detect anomalies, the motion vectors in the compressed video and the region information of the motion vectors were used. This information was provided as input to the autoencoder model, which is an unsupervised artificial neural network method, and thus the model was trained. The trained model was tested on video data containing anomalies. As output, during the streaming of any video, it is provided to draw a regularity score graph and display the anomaly regions by color. In this paper, we propose an autoencoder based method for anomaly detection in compressed video instead of the original uncompressed video.",

keywords = "Anomaly detection, Compressed domain video analysis, Compressed video, Motion vector",

author = "Sumeyye Cavas and Beratoglu, \{Muhammet Sebul\} and Toreyin, \{Behcet Ugur\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 ; Conference date: 09-06-2021 Through 11-06-2021",

year = "2021",

month = jun,

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doi = "10.1109/SIU53274.2021.9478048",

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series = "SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings",

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Cavas, S, Beratoglu, MS & Toreyin, BU 2021, Sikistmlrrus videoda anomali tespiti. in SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings., 9478048, SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings, Institute of Electrical and Electronics Engineers Inc., 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021, Virtual, Istanbul, Türkiye, 9/06/21. https://doi.org/10.1109/SIU53274.2021.9478048

Sikistmlrrus videoda anomali tespiti. / Cavas, Sumeyye; Beratoglu, Muhammet Sebul ; Toreyin, Behcet Ugur.
SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. 9478048 (SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - Sikistmlrrus videoda anomali tespiti

AU - Cavas, Sumeyye

AU - Beratoglu, Muhammet Sebul

AU - Toreyin, Behcet Ugur

PY - 2021/6/9

Y1 - 2021/6/9

N2 - In this paper, an anomaly detection approach has been developed on video compressed in H.265 format. In order to detect anomalies, the motion vectors in the compressed video and the region information of the motion vectors were used. This information was provided as input to the autoencoder model, which is an unsupervised artificial neural network method, and thus the model was trained. The trained model was tested on video data containing anomalies. As output, during the streaming of any video, it is provided to draw a regularity score graph and display the anomaly regions by color. In this paper, we propose an autoencoder based method for anomaly detection in compressed video instead of the original uncompressed video.

AB - In this paper, an anomaly detection approach has been developed on video compressed in H.265 format. In order to detect anomalies, the motion vectors in the compressed video and the region information of the motion vectors were used. This information was provided as input to the autoencoder model, which is an unsupervised artificial neural network method, and thus the model was trained. The trained model was tested on video data containing anomalies. As output, during the streaming of any video, it is provided to draw a regularity score graph and display the anomaly regions by color. In this paper, we propose an autoencoder based method for anomaly detection in compressed video instead of the original uncompressed video.

KW - Anomaly detection

KW - Compressed domain video analysis

KW - Compressed video

KW - Motion vector

UR - https://www.scopus.com/pages/publications/85111438761

U2 - 10.1109/SIU53274.2021.9478048

DO - 10.1109/SIU53274.2021.9478048

M3 - Konferans katkısı

AN - SCOPUS:85111438761

T3 - SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings

BT - SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021

Y2 - 9 June 2021 through 11 June 2021

ER -

Cavas S, Beratoglu MS , Toreyin BU. Sikistmlrrus videoda anomali tespiti. In SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. 9478048. (SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings). doi: 10.1109/SIU53274.2021.9478048

Sikistmlrrus videoda anomali tespiti

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

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