TDSD: Text-Driven Scene-Decoupled Weakly Supervised Video Anomaly Detection

Video anomaly detection has garnered widespread attention in industry and academia in recent years due to its significant role in public security. However, many existing methods overlook the influence of scenes on anomaly detection. These methods simply label the occurrence of certain actions or objects as anomalous. In reality, scene context plays a crucial role in determining anomalies. For example, running on a highway is anomalous, while running on a playground is normal. Therefore, understanding the scene is essential for effective anomaly detection. In this work, we aim to address the challenge of scene-dependent weakly supervised video anomaly detection by decoupling scenes. Specifically, we propose a novel text-driven scene-decoupled (TDSD) framework, consisting of a TDSD module (TDSDM) and fine-grained visual augmentation (FVA) modules. The scene-decoupled module extracts semantic information from scenes, while the FVA module assists in fine-grained visual enhancement. We validate the effectiveness of our approach by constructing two scene-dependent datasets and achieve state-of-the-art results on scene-agnostic datasets as well. Code is available at https://github.com/shengyangsun/TDSD.