## 2 Related Work
The study also emphasizes recent advancements in spatio-temporal detection, which involves localizing actions across both temporal and spatial dimensions, making it particularly relevant for video analysis applications, such as surveillance and security monitoring. Within this context, learning methods are categorized based on the type of supervision employed. Methods According to the Type of Learning
1. Fully Supervised Methods: These methods require precise labels for each action within the training data. While they achieve high accuracy, they often face limitations due to the scarcity of accurately labeled large datasets. Many of these techniques focus on spatio-temporal approaches to action detection [26; 27; 28; 29]. 2. Weakly Supervised Methods: These methods operate with labels that may be imprecise or exist only at the video level. This approach allows for training on larger datasets due to the increased availability of data [30; 31; 32; 33]. 3. Unsupervised Methods: These models learn patterns without explicit labels, making them valuable when precise labels are unavailable or when the objective is to explore unexpected patterns in the data [34; 35]. 4. Semi-Supervised Methods: These methods leverage both labeled and unlabeled data to enhance performance. They are particularly effective in situations where only a limited amount of labeled data is available [36; 37; 38]. 5. Self-supervised methods: The model is trained to learn from its own predictions, which can be beneficial when precise labels are scarce [39; 40; 41]. Several studies have aimed to provide researchers with tools for addressing anomaly detection. One notable example is the LAD dataset, developed as a benchmark for anomaly detection in video sequences. It comprises 2000 video sequences featuring 14 categories of anomalies, such as crashes, fires, violence, and robberies. The creation of this dataset was motivated by the need for larger databases with more detailed labels (at both the video and frame levels) to facilitate the accurate detection of anomalous events. Public sources, including YouTube and YouKu, were utilized to collect these videos. Testing a model based on a 3D deep neural network on this dataset yielded satisfactory results, achieving an accuracy exceeding 86%, which underscores the utility of the dataset for anomaly detection research and its potential application in public security. Another noteworthy example is the work by [42], which proposes a new benchmark for video anomaly detection by framing the task as a supervised classification problem in an \"open-set\" context. This approach involves incorporating both normal and abnormal events during the training phase, while the anomalies detected during inference belong to distinct, previously unseen categories. The UBnormal dataset includes 29 virtual scenes with a total of 236,880 frames, with anomalies annotated at the pixel level to facilitate fully supervised methods. Experimental results demonstrated that UBnormal can enhance the performance of existing anomaly detection models, even when applied to real-world datasets. Performance improvements of up to 1.3% in Area Under the Curve (AUC) were observed, highlighting the relevance of UBnormal in advancing research on video anomaly detection. The quality and relevance of datasets are fundamental to the success of any machine learning model. Intelligent systems depend on robust and diverse datasets to perform complex tasks, such as facial recognition, product recommendations, and disease prediction. These datasets enable models to learn underlying patterns and generalize their performance to previously unseen scenarios. However, one of the primary challenges in this domain is the scarcity of accurate and comprehensively labeled data. Labeled datasets are particularly crucial for training and validating deep learning models, especially in critical applications such as crime detection in video footage.