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Title	Teg: Temporal-Granularity Method For Anomaly Detection With Attention In Smart City Surveillance
ID_Doc	54663
Authors	Akdag E.; Bondarev E.; De With P.H.N.
Year	2024
Published	2024 IEEE International Conference on Visual Communications and Image Processing, VCIP 2024
DOI	http://dx.doi.org/10.1109/VCIP63160.2024.10849819
Abstract	Anomaly detection in video surveillance has recently gained interest from the research community. Temporal duration of anomalies vary within video streams, leading to complications in learning the temporal dynamics of specific events. This paper presents a temporal-granularity method for an anomaly detection model (TeG) in real-world surveillance, combining spatio-temporal features at different time-scales. The TeG model employs multi-head cross-attention (MCA) blocks and multi-head self-attention (MSA) blocks for this purpose. Additionally, we extend the UCF-Crime dataset with new anomaly types relevant to Smart City research project. The TeG model is deployed and validated in a city surveillance system, achieving successful real-time results in industrial settings. © 2024 IEEE.
Author Keywords	abnormal behaviour; attention; computer vision; surveillance; temporal granularity

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