## 1 Introduction  
The use of surveillance cameras in private and public spaces has become increasingly prevalent in recent years for various purposes, including tracking, monitoring, and preventing violations. An anomaly, as defined in the surveillance field, refers to a deviation from common rules, types, arrangements, or forms and can be characterized as an uncommon event that deviates from \"normal\" behavior. Detecting anomalies in surveillance videos is crucial to maintaining security in various applications, such as crime detection, accident detection, abandoned object detection, illegal activity detection, and parking area monitoring. However, the manual detection of anomalies in surveillance videos is a tedious and labor-intensive task for humans. This is due to the large amount of data generated by critical systems in security applications, making manual analysis an impractical solution. In recent years, there has been a significant increase in the demand for automated systems for detecting video anomalies. These systems include biometric identification of individuals, alarm-based monitoring of Closed-Circuit Television (CCTV) scenes, automatic detection of traffic violations, and video-based detection of abnormal behavior [1]. Automated systems significantly reduce human labor and time, making them more efficient and cost-effective for detecting anomalies in surveillance videos. Identifying and tracking anomalies in the recorded video comprise a growing research problem in surveillance. Many methods have been proposed by researchers in academia, including the use of machine learning algorithms and image processing techniques. In recent times, the utilization of surveillance cameras in public and private areas has risen significantly, serving multiple objectives. Identifying abnormalities in surveillance footage is vital to upholding safety measures in diverse scenarios. Nonetheless, the manual identification of anomalies in surveillance footage can be arduous and laborious for humans. Researchers have proposed automated systems for detecting video anomalies to address this issue, significantly reducing human labor and time. Despite the advancements that have been made, there is still room for improvement in the accuracy, reliability, and scalability in developing a flawless video surveillance system [2]. The Surveillance Video Anomaly Detection (SVAD) system is a sophisticated technology designed to detect unusual or suspicious behavior in video surveillance footage without human intervention. The system operates by analyzing the video frames and identifying deviations from normal patterns of movement or activity. This is achieved through advanced algorithms and machine learning techniques that can detect and analyze the position of pixels in the video frame at the time of an event. Traditionally, anomaly detection methods have focused on identifying objects that deviate from normal trajectories. However, these methods need to be improved [3] for use in video surveillance due to the variety of objects that may be present in a video frame. As a result, two main approaches have been developed for video anomaly detection. The first approach involves measuring the magnitude of the error by calculating the reconstruction error of future frames. This is achieved by comparing the predicted future frames with the actual frames and identifying significant differences. The second approach involves predicting the future frames based on the previous frames and assigning a high anomaly score to any frame that deviates significantly from the predicted frame. In recent years, with the advancement of hardware performance and the development of new models, smart learning techniques have become increasingly popular in video anomaly detection. However, the use of these techniques also brings several challenges. One of the major challenges is the production of big data, which requires a large amount of computational power for processing.