# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 1 Introduction  
Anomaly detection is essential in fields such as surveillance, healthcare, finance, and industrial monitoring, where identifying deviations from normal patterns is crucial. In video surveillance, anomalies can include unusual movements, unauthorized access, or unexpected incidents, with context playing a significant role in determining what is considered abnormal, for example, walking on a highway instead of a sidewalk. Since obtaining labeled video data for anomalies is challenging, many existing methods approach anomaly detection in video surveillance as a one-class classification problem, where models are trained solely on normal activity and expected to flag deviations as anomalies during testing. However, this approach presents several critical challenges when applied to complex,real-world environments. First, existing models often lack robustness to environmental variability, struggling to handle conditions such as changes in lighting, weather, or camera motion--factors commonly encountered across surveillance scenarios like highways, parks, offices, and warehouses. Second, the limited diversity of current datasets can result in poor anomaly representation, causing common activities like running or cycling on a road or sidewalk to be misclassified. Third, most anomaly detection frameworks are human-centric, focusing mainly on activities like assault, fighting, or robbery, while overlooking non-human anomalies such as fires, falling objects, explosions, water leaks, or traffic accidents. Fourth, a significant gap exists in scenario diversity: many datasets include only different camera angles within a single environment, lacking the cross-scenario variability needed to train generalizable models. To address these gaps, an MSAD dataset [1] was used that incorporates 11 diverse anomaly types--including assault, explosion, fighting, fire, objects falling, people falling, robbery, shooting, traffic accident, vandalism, and water incidents--across 14 distinct normal surveillance scenarios such as front door areas, highways, malls, offices, parks, restaurants, parking lots, pedestrian streets, roads, shops, sidewalks, street highways, trains, and warehouses. This combination introduces substantial variability in both scene context and anomaly nature, underscoring the need for adaptive approaches like few-shot meta-learning that can effectively generalize across such a wide range of spatial patterns. In addition to the dataset, this study uses MAML [2; 3] as a novel approach to anomaly detection. Meta-learning, or \"learning to learn,\" enables models to quickly adapt to new tasks with minimal data, which is particularly valuable when large-scale data collection is impractical. The meta-learning process consists of two phases: the meta-training phase, where the model is exposed to various tasks (i.e., different scenarios in the MSAD dataset) to develop a meta-model capable of adapting to new tasks, and the meta-testing phase, where the model is evaluated on previously unseen tasks to assess its ability to generalize. This meta-learning approach offers several advantages for anomaly detection. It allows models to rapidly adapt to new environments with minimal data, improving their ability to function in dynamic settings. Training in diverse tasks enhances the model's ability to generalize, making it more effective in identifying anomalies in unfamiliar scenarios. Additionally, meta-learning increases efficiency by leveraging small amounts of anomalous data, addressing the common issue of data scarcity. Applying MAML to the MSAD dataset enables the model to handle new viewpoints by learning from multiple camera perspectives within the same scenario. It also demonstrates strong generalization capabilities when applied to entirely new scenarios, proving its effectiveness in real-world applications. This approach significantly strengthens anomaly detection in dynamic and evolving environments, making it a promising solution for improving security and monitoring systems.