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:由于异常事件固有的罕见性和异质性,这一问题被视为一种正常模式战略,我们模型学习以物体为中心的正常模式,而没有在培训期间看到异常样本,其主要贡献在于将预先训练的物体级行动原型与直径偏差估计功能结合在一起,从而通过对以重建为基础的主流战略引入额外的制约来扩大先前的方法。我们的框架利用外观和运动信息来学习物体级行为,并在记忆模块中捕捉典型模式。关于几个众所周知的数据集的实验表明,我们的方法的效力在于它超越了当前最相关的随机时空评价指标的先进水平。