2022/03/07 by Bergaoui, Khalil, Naji, Yassine, Setkov, Aleksandr +3
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.2203.03677
This paper addresses video anomaly detection problem for videosurveillance. Due to the inherent rarity and heterogeneity of abnormal events, the problem is viewed as a normality modeling strategy, in which our model learns object-centric normal patterns without seeing anomalous samples during training. The main contributions consist in coupling pretrained object-level action features prototypes with a cosine distance-based anomaly estimation function, therefore extending previous methods by introducing additional 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.