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.

翻译：本文针对视频监控中的异常检测问题展开研究。由于异常事件天然具有稀有性和异质性，我们将该问题视为一种正常性建模策略：模型在训练阶段无需接触异常样本，即可学习面向目标的正常模式。主要创新在于将预训练的目标级动作特征原型与基于余弦距离的异常估计函数相结合，通过引入额外约束拓展了主流重构策略方法。本框架同时利用外观与运动信息学习目标级行为特征，并在记忆模块中捕捉原型模式。在多个知名数据集上的实验表明，该方法在时空评估指标上全面超越现有先进方法，验证了其有效性。