- Book Chapter
- 10.1007/978-3-030-89128-2_35
Object-Centric Anomaly Detection Using Memory Augmentation
- Jan 01, 2021
- Jacob Velling Dueholm + 2 more +2
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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