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Communication Dans Un Congrès Année : 2022

Object-Centric And Memory-Guided Normality Reconstruction For Video Anomaly Detection

Résumé

This paper addresses the anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, this problem is tackled from a normality modeling perspective, where our model learns object-centric normal patterns without seeing anomalous samples during training. Our main contributions consist in coupling objectlevel action features with a cosine distance-based anomaly estimation function. We therefore extend previous methods by introducing explicit geometric constraints to the mainstream reconstruction-based strategy. Our framework leverages both appearance and motion information to learn object-level behavior and captures prototypical patterns within a memory module. Experiments on several well-known datasets demonstrate the effectiveness of our method as it outperforms current state-of-the-art on most relevant spatio-temporal evaluation metrics.
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Dates et versions

hal-03880897 , version 1 (01-12-2022)

Identifiants

Citer

Khalil Bergaoui, Yassine Naji, Aleksandr Setkov, Angelique Loesch, Michèle Gouiffès, et al.. Object-Centric And Memory-Guided Normality Reconstruction For Video Anomaly Detection. 2022 IEEE International Conference on Image Processing (ICIP), Oct 2022, Bordeaux, France. pp.2691-2695, ⟨10.1109/ICIP46576.2022.9897259⟩. ⟨hal-03880897⟩
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