## 6 Discussion
### Future Directions  
Future research problems are selecting the proper video features, selecting the best classifiers for improved performance in anomaly detection, and creating an appropriate performance-cost balance.  
SVAD has benefited from the successful application of UL techniques. However, to further enhance the performance of these methods, it may be beneficial to supplement them with other techniques, such as image processing or SL. By combining these different methods, we can potentially improve the accuracy and effectiveness of anomaly detection in videos. Moreover, UL techniques enable machine learning from unlabeled videos,eliminating the need for manually tagging significant amounts of data, as required in SL methods. As future studies focus on training DNNs, it is crucial to prioritize approaches that require attention from a human.  
Research can focus on improving prediction models to capture complex spatiotemporal patterns in surveillance videos. This can involve integrating attention mechanisms, graph-based models, or other innovative methods to enhance accuracy and robustness. Another area of study is developing more resilient reconstruction models that can effectively handle common difficulties such as occlusions and lighting changes. This could entail incorporating techniques such as adversarial training or domain adaptation to improve the robustness of the reconstruction process.  
In this field, researchers commonly use the \"UCSD Ped2\" and \"CUHK Avenue\" datasets, which were recorded outdoors. However, it is apparent that we need to improve the quantity and quality of datasets that include both synthetic abnormal events created within specific scenarios and real-world abnormal events captured by actual surveillance cameras. To improve video surveillance, we need higher-resolution datasets and greater diversity in anomaly detection techniques.  
Integrating SVAD with other technologies can further enhance its capabilities. For instance, combining video analytics with sensor data from sources such as audio, temperature, or biometric data can provide a more comprehensive and precise understanding of anomalous events.  
Achieving effective SVAD over edge networks is challenging due to the computational cost of deep learning models. Future directions should focus on optimizing online learning techniques for edge devices to enable real-time SVAD in resource-constrained environments.  
To improve the flexibility and efficiency of SVAD models, they can be made adaptable to diverse data by incorporating parameters such as object/individual locations, distances, and motion trajectories into the model's design. This will enable real-time detection of anomaly patterns in video frames, including those that have not been seen before.  
Incorporating human feedback and expertise is crucial for effective anomaly detection in human-in-the-loop systems. One possible avenue for future development is the use of human-in-the-loop surveillance video anomaly detection, which would allow operators to provide feedback, refine the model, and contribute their domain-specific knowledge. This approach has the potential to yield more precise and relevant anomaly detection results that are tailored to specific contexts.  
As technology continues to advance, we can expect to see a proliferation of anomaly detection models designed for specific domains. These models will rely on domain-specific data to pinpoint unusual patterns and behaviors that may signal potential issues. By incorporating industry-specific characteristics, these models will offer even greater accuracy, providing more nuanced insights to support better decision-making and outcomes.