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cs.CV2025
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos
Yiling Zhang, Erkut Akdag, Egor Bondarev +1
Detection of anomaly events is relevant for public safety and requires a combination of fine-grained motion information and contextual events at variable time-scales. To this end,…
cs.CV2024
TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance
Erkut Akdag, Egor Bondarev, Peter H. N. De With
Anomaly detection in video surveillance has recently gained interest from the research community. Temporal duration of anomalies vary within video streams, leading to complications…
cs.CV2024
uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories
Giacomo D'Amicantonio, Egor Bondarau, Peter H. N. de With
Deep learning-based approaches have achieved significant improvements on public video anomaly datasets, but often do not perform well in real-world applications. This paper address…