## 6 Discussion
### Limitations and Challenges  
The field of SVAD poses several challenges, such as processing vast amounts of video data, performing real-time analysis, and distinguishing normal from abnormal behavior. Efficient algorithms that utilize parallel computing and GPU acceleration are necessary to meet the computational demands of processing large volumes of surveillance video data in real-time. Additionally, distinguishing normal from abnormal behavior across different contexts and types is another key challenge in SVAD. Machine learning models can be trained to identify patterns and features in the video data, enabling them to differentiate between normal and abnormal behavior. False detection poses a major obstacle to the effective implementation of SVAD systems, as it can be triggered by changes in illumination, camera motion, occlusions, and scene clutter [10]. To mitigate this challenge, a range of techniques can be applied, including feature extraction, anomaly detection algorithms, and deep-learning-based methods. Additionally, strategies such as temporal modeling and background subtraction are employed to minimize the occurrence of false detections. The behavior of people and objects can vary significantly, making it challenging to identify what constitutes an anomaly. For example, a person running in a park is normal behavior, but the same person running in a shopping mall may be considered abnormal. Therefore, the detection system must be able to learn and distinguish between different behaviors to identify anomalies accurately. Tracking individuals and objects and identifying anomalies in their behavior is a complex task, particularly in challenging environments. Algorithms may struggle with accurately tracking and identifying objects, leading to errors and false positives. To achieve greater accuracy, sophisticated algorithms must be employed to analyze and interpret data. Imbalanced datasets, where normal activities occur more frequently than anomalous events, can lead to model bias toward predicting normal activities. Deep learning models can automatically extract features from video frames and learn to detect anomalies despite differing lighting conditions, camera angles, and object sizes. Additionally, transfer learning techniques can adapt models trained on one dataset to another with similar characteristics, reducing the need for large amounts of labeled data. Researchers face several challenges when working with SVAD datasets. One major challenge is the need for ground truth video sequences to analyze the data accurately. However, creating and annotating these sequences with the required level of detail is a very time-consuming task. Additionally, there are other obstacles, such as a shortage of training samples and annotations, a lack of diversity in terms of scenes and viewing angles, the exclusion of adverse weather conditions and varying illumination, insufficient coverage of anomaly events, and the limitations of camera devices [120]. These factors hinder the development of accurate and reliable SVAD analysis models. Environmental noise and inadequate resilience make it more difficult to distinguish between normal and abnormal occurrences, resulting in false alarms or the failure to notice some events. However, deep learning techniques have shown potential in automatically learning important data features. Feature extraction and ensemble methods have also been utilized to develop effective anomaly detection systems that consider the challenging and noisy environments in which they will be utilized. SVAD involves analyzing features within a video scene to identify patterns and detect anomalies. However, processing features that do not fit the expected patterns can lead to unnecessary computation, resulting in slower and less accurate detection. Therefore, selecting the appropriate features is crucial for effective anomaly detection in videos.