## 6 Discussion
### Current Developments  
Significant advancements have been witnessed in the field of SVAD in recent years with the use of AI techniques. This sub-section discusses current developments in AI techniques in SVAD. UL and SSL algorithms (AE, GAN, etc.) are more successful than SL algorithms (GMM, HMM, etc.) in SVAD. This is due to the lack of labeled data, the presence of unknown anomalies, the flexibility to adapt to different situations, and the ability to handle large datasets. SL algorithms require labeled data for training, making it difficult to apply them in scenarios where labeled data are scarce. UL and SSL algorithms do not require labeled data and can detect unknown anomalies, making them more flexible in adapting to different situations. Additionally, they can handle large amounts of data, making them more suitable for surveillance video feeds that generate massive amounts of data. AI techniques, such as CNNs, LSTMs, and GANs, are extensively employed in the field of SVAD and have demonstrated highly promising outcomes. In particular, GANs can learn from large datasets and generate new data similar to the original, making them particularly well-suited to detecting anomalies in surveillance footage where unusual events may not have been previously observed. This approach, known as generative modeling, can be more effective than traditional supervised learning methods that rely on labeled data, which can be scarce and costly to obtain. By training a GAN on a large dataset of normal activities, it is possible to generate a model that can identify anomalous behavior in real-time. These techniques can learn complex patterns and relationships directly from the data. This allows them to detect subtle anomalies that may be missed by traditional statistical or classification-based algorithms. Reconstruction-based (CAE, GAN, etc.) and prediction-based (LSTM, ViT, etc.) algorithms are more effective than statistics-based (GMM, etc.) and classification-based (kNN, etc.) algorithms in SVAD in terms of their robustness, flexibility, and efficiency. These algorithms are designed to learn and extract features from video data, making them less sensitive to irrelevant information and better able to adapt to changing environmental conditions. In addition, these algorithms are efficient in processing large amounts of data quickly, which makes them suitable for surveillance applications that require real-time anomaly detection. On the other hand, statistics-based and classification-based algorithms may require significant computational resources and may not be as effective at detecting anomalies in real-time. Overall, reconstruction-based and prediction-based algorithms offer better performance and reliability in SVAD. Furthermore, there has been a growing interest in developing real-time anomaly detection systems that can detect anomalies in surveillance videos in real-time. Real-time anomaly detection is important for critical applications, such as security and public safety, where the timely detection of anomalies can prevent serious incidents. SVAD's lack of available datasets can be attributed to several factors, including concerns around data privacy, the high cost and time requirements for collecting and labeling data, difficulties in detecting anomalies in real-world scenarios, and a general lack of standardization within the field. These factors pose significant challenges for the development and evaluation of algorithms in SVAD. However, there are initiatives underway to address these challenges. Some of these initiatives include the creation of privacy-preserving data collection techniques and standardized datasets for benchmarking. Determining which dataset is better can be challenging due to the presence of global and local features that indicate abnormal events. Local features are emphasized in datasets such as UCSD, ShanghaiTech, and Subway, while global features are more prominent in UCF-Crime.