Sikistmlrrus videoda anomali tespiti

Sumeyye Cavas; Muhammet Sebul Beratoglu; Behcet Ugur Toreyin

doi:10.1109/SIU53274.2021.9478048

Sikistmlrrus videoda anomali tespiti

Translated title of the contribution: Anomaly detection in compressed video

Sumeyye Cavas, Muhammet Sebul Beratoglu, Behcet Ugur Toreyin

Informatics Institute

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Translated title of the contribution	Anomaly detection in compressed video
Original language	Turkish
Title of host publication	SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665436496
DOIs	https://doi.org/10.1109/SIU53274.2021.9478048
Publication status	Published - 9 Jun 2021
Event	29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 - Virtual, Istanbul, Turkey Duration: 9 Jun 2021 → 11 Jun 2021

Publication series

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

Conference

Conference	29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021
Country/Territory	Turkey
City	Virtual, Istanbul
Period	9/06/21 → 11/06/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Access to Document

10.1109/SIU53274.2021.9478048

Cite this

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 Article 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",

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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, Turkey, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Sikistmlrrus videoda anomali tespiti

AU - Cavas, Sumeyye

AU - Beratoglu, Muhammet Sebul

AU - Toreyin, Behcet Ugur

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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

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M3 - Konferans katkısı

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

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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

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