Industry surveillance is widely applicable in sectors like retail, manufacturing, education, and smart cities, each presenting unique anomalies requiring specialized detection. However, adapting anomaly detection models to novel viewpoints within the same scenario poses challenges. Extending these models to entirely new scenarios necessitates retraining or fine-tuning, a process that can be time consuming. To address these challenges, we propose the Scenario-Adaptive Anomaly Detection (SA2D) method, leveraging the few-shot learning framework for faster adaptation of pre-trained models to new concepts. Despite this approach, a significant challenge emerges from the absence of a comprehensive dataset with diverse scenarios and camera views. In response, we introduce the Multi-Scenario Anomaly Detection (MSAD) dataset, encompassing 14 distinct scenarios captured from various camera views. This real-world dataset is the first high-resolution anomaly detection dataset, offering a solid foundation for training superior models. MSAD includes diverse normal motion patterns, incorporating challenging variations like different lighting and weather conditions. Through experimentation, we validate the efficacy of SA2D, particularly when trained on the MSAD dataset. Our results show that SA2D not only excels under novel viewpoints within the same scenario but also demonstrates competitive performance when faced with entirely new scenarios. This highlights our method's potential in addressing challenges in detecting anomalies across diverse and evolving surveillance scenarios.

翻译：工业监控广泛应用于零售、制造业、教育及智慧城市等领域，每个场景均存在独特的异常现象，需要专门化的检测方法。然而，将异常检测模型适配至同一场景中的新型视角面临挑战，而将模型扩展至全新场景则需要耗时重新训练或微调。为解决这些问题，我们提出场景自适应异常检测（SA2D）方法，借助少样本学习框架实现预训练模型对新概念的快速适配。尽管该方法行之有效，但缺乏包含多样化场景与摄像头视角的综合数据集成为重大瓶颈。为此，我们引入多场景异常检测（MSAD）数据集，涵盖来自不同摄像头视角的14个独特场景。该真实世界数据集是首个高分辨率异常检测数据集，为训练更优模型提供了坚实基础。MSAD包含多样化的正常运动模式，并纳入光照与天气条件变化等具有挑战性的差异。通过实验验证，SA2D在MSAD数据集上训练时展现出优异效能。结果表明，SA2D不仅在同场景新型视角下表现出色，面对全新场景时亦展现出竞争性性能，凸显了该方法在应对多样且动态变化的监控场景中异常检测挑战的潜力。