Increasing scene-awareness is a key challenge in video anomaly detection
(VAD). In this work, we propose a hierarchical semantic contrast (HSC) method
to learn a scene-aware VAD model from normal videos. We first incorporate
foreground object and background scene features with high-level semantics by
taking advantage of pre-trained video parsing models. Then, building upon the
autoencoder-based reconstruction framework, we introduce both scene-level and
object-level contrastive learning to enforce the encoded latent features to be
compact within the same semantic classes while being separable across different
classes. This hierarchical semantic contrast strategy helps to deal with the
diversity of normal patterns and also increases their discrimination ability.
Moreover, for the sake of tackling rare normal activities, we design a
skeleton-based motion augmentation to increase samples and refine the model
further. Extensive experiments on three public datasets and scene-dependent
mixture datasets validate the effectiveness of our proposed method.

本文提出了一种基于层次语义对比的视频异常检测模型，结合了前景对象和背景场景特征，通过自动编码器框架增强编码的潜在特征，同时使用对象级和场景级对比学习，增强模型的判别能力，并通过基于骨架的运动增强来进一步处理少见的正常活动。经过在三个常规和场景相关混合数据集上的实验证明了这种方法的有效性。