Ositola Martins Osifeko

The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process.

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[1] Martins Osifeko, Gerhard Hancke, Adnan Abu-Mahfouz, "SurveilNet", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/4gnf-a488. Accessed: May. 23, 2024.
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doi = {10.21227/4gnf-a488},
url = {http://dx.doi.org/10.21227/4gnf-a488},
author = {Martins Osifeko; Gerhard Hancke; Adnan Abu-Mahfouz },
publisher = {IEEE Dataport},
title = {SurveilNet},
year = {2021} }
TY - DATA
T1 - SurveilNet
AU - Martins Osifeko; Gerhard Hancke; Adnan Abu-Mahfouz
PY - 2021
PB - IEEE Dataport
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Martins Osifeko, Gerhard Hancke, Adnan Abu-Mahfouz. (2021). SurveilNet. IEEE Dataport. http://dx.doi.org/10.21227/4gnf-a488
Martins Osifeko, Gerhard Hancke, Adnan Abu-Mahfouz, 2021. SurveilNet. Available at: http://dx.doi.org/10.21227/4gnf-a488.
Martins Osifeko, Gerhard Hancke, Adnan Abu-Mahfouz. (2021). "SurveilNet." Web.
1. Martins Osifeko, Gerhard Hancke, Adnan Abu-Mahfouz. SurveilNet [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/4gnf-a488
Martins Osifeko, Gerhard Hancke, Adnan Abu-Mahfouz. "SurveilNet." doi: 10.21227/4gnf-a488