# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 2 Related Works
### Anomaly Detection Methods  
[PERSON] et al. 2019 [10] studied reconstruction-based techniques, such as those employing Auto-Encoder methods exclusively trained on normal videos and subsequently tested on both normal and abnormal videos. [PERSON] et al. 2020 [11] presented a novel approach to anomaly detection through a classification-based method called GOAD, which addressed the limitations of existing techniques by utilizing a semi-supervised learning framework. It was built on previous works that categorize anomaly detection methods into reconstruction, distributional, and classification-based approaches, highlighting the effectiveness of deep learning in this domain. [PERSON] et al. 2021 [12] studied anomaly detection as a one-class classification problem, relying on the features of normal events where a generative adversarial network aims to reconstruct normal events to minimize the reconstruction loss. [PERSON] et al. 2024 [13] introduced a meta-learning framework for efficient anomaly diagnosis in few-shot AIOps scenarios, addressing the challenges of limited labeled data in IT operations. [PERSON] et al. 2024 [14] presented a novel approach for detecting pipeline leaks using limited normal data, particularly in hazardous environments like power plants. They proposed the GAD-PN framework, combining Cycle GAN for generating synthetic anomaly data and Prototypical Networks for anomaly classification based on few-shot learning. [PERSON] et al. 2023 [15] proposed a novel zero-shot anomaly detection method leveraging batch normalization statistics to detect anomalies without requiring training data from the target distribution. Their approach models the distribution of batch normalization parameters across diverse tasks, enabling effective generalization to unseen domains. [PERSON] et al., 2023 [16] proposed an explainable few-shot learning framework for online anomaly detection in ultrasonic metal welding processes with varying configurations. Their approach combined prototype-based learning with interpretability techniques to enable accurate detection and transparent decision making using limited labeled data. [PERSON] et al. 2023 [17] proposed an anomaly detection method using a model-agnostic meta-learning-based variational autoencoder (MAVAE) tailored to facility management systems. This approach enables rapid adaptation to new environments using few-shot learning by leveraging MAML to fine-tune VAE parameters with limited normal data. [PERSON] et al. 2023 [18] introduced Hydra, a meta-learning framework designed for fast model recommendation in unsupervised multivariate time series anomaly detection. Their approach leverages a small set of meta-features to match datasets with suitable detection models, addressing the challenges of scalability and generalization. [PERSON] et al. 2021 [19] introduced an unsupervised transformer-based framework, Meta Former, for anomaly detection without relying on labeled data. The model leverages self-attention mechanisms to learn generalized representations from normal patterns, enabling it to detect anomalies across diverse domains. [PERSON] et al. 2022 [20] studied and experimented with anomaly detection methods, which were often thought to struggle with understanding normal events, although some models can effectively detect anomalies. However, achieving a good reconstruction is not a guarantee of effective anomaly detection. These methods can be sensitive to object speeds, leading them to misclassify sudden motions as anomalies. [PERSON] and [PERSON] et al. 2020 [21] published a paper that introduced a meta-transfer learning framework that could be adapted for anomaly detection tasks. The method aims to learn a meta-learner that can transfer knowledge from related tasks to new tasks with limited labeled data. [PERSON] et al. 2020 [22] addressed the limitations of traditional anomaly detection methods in cybersecurity, emphasizing the potential of meta-learning to enhance detection accuracy and adaptability with limited data.