# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 2 Related Works
### Anomaly Detection Methods
Meta-learning outperformed traditional models, especially with limited data. Initial training was resource-intensive, but adaptation to new tasks was efficient. [PERSON] et al. 2022 [23] conducted a systematic review of anomaly detection methods utilizing machine learning and deep learning techniques across various domains. The study categorized approaches based on supervised, unsupervised, and semi-supervised learning, highlighting their strengths, limitations, and application areas. [PERSON] et al. 2020 [24] proposed a novel few-shot learning framework leveraging MAML, which was proposed for bearing anomaly detection. The framework aimed to develop a robust fault classifier with minimal data by adapting quickly to new fault conditions. In a case study involving the introduction of new artificial faults, the method achieved up to 25 percent overall accuracy compared to a Siamese network benchmark. Moreover, when sufficient training data from artificial damages was available, MAML demonstrated competitive generalization abilities compared to state-of-the-art few-shot learning methods in identifying realistic bearing damages. [PERSON], [PERSON] et al. 2024 [1] developed a SAAD (Scenario Adaptive Anomaly Detection) model that takes a unique approach, utilizing a few-shot learning framework for faster adaptation to novel concepts and scenarios. This adaptability, coupled with its competitive performance on diverse scenarios, sets SAAD apart from traditional models. [PERSON] et al. 2024 [25] provided a comprehensive review of deep learning techniques for video anomaly detection, highlighting advancements across supervised, unsupervised, and self-supervised methods. The paper categorized key approaches based on learning paradigms, feature representations, and anomaly scoring techniques. [PERSON] et al. 2024 [26] proposed ADAGENT, a novel anomaly detection framework that leverages multimodal large models to operate effectively in adverse and complex environments. By integrating visual, textual, and contextual data, ADAGENT enhances anomaly detection robustness under challenging conditions such as low visibility or sensor noise. [PERSON], [PERSON] et al. 2024 [27] proposed the Scenario-Adaptive Anomaly Detection (SA2D) model to enhance few-shot learning adaptability across diverse scenarios and viewpoints. Leveraging a meta-learning framework, SA2D effectively adapts to unseen contexts. It uses future frame prediction for anomaly detection, supported by U-Net and ConvLSTM for temporal modeling and a GAN for frame reconstruction. The authors also introduced the MSAD dataset, featuring frame-level annotations across varied indoor/outdoor scenes and multiple camera angles, addressing the limitations of datasets like UCSD Ped1/Ped2 and Shanghai Tech (Shanghai, China). The study emphasized the role of scenario diversity and meta-learning in advancing video anomaly detection. In contrast to existing methods, meta-learning framework can be utilized in models to adapt to multiple scenarios and even new scenarios with more accuracy. This includes MAML, which trains a model's parameters in such a way that a small number of gradient updates can quickly adapt to a new task with few examples. This approach is versatile and can be applied to various domains, including image classification, regression, and reinforcement learning.