Abstract
Video anomaly detection is becoming of increased interest as surveillance is becoming more widespread. We propose an object-centric method with memory augmentation (ObjMemAE) for video anomaly detection. Recently, object-centric approaches is seen at the top of the leaderboards, where we take the novel approach of combining an object-centric approach with memory augmentation using a long term memory bank storing prototypical objects. The memory module also allows the use of additional object-centric features. The proposed method is shown to outperform the baseline by 4.5%, with an AUC score of 98.3% on the UCSD-Ped2 dataset achieving state-of-the-art.
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This work was supported by the Milestone Research Programme at Aalborg University.
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Dueholm, J.V., Nasrollahi, K., Moeslund, T.B. (2021). Object-Centric Anomaly Detection Using Memory Augmentation. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13052. Springer, Cham. https://doi.org/10.1007/978-3-030-89128-2_35
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