## 1 Introduction  
Video surveillance systems have become an essential component of our security infrastructure, playing a critical role in monitoring and ensuring safety in various settings, like public spaces, transportation systems, and commercial establishments. Since surveillance systems are used in various areas and environments, it has always been difficult to identify abnormal activity accurately and quickly in recordings. Further, more than 1 billion security cameras were used worldwide in 2023 [1], and that number is projected to exceed 2.24 billion by 2030, with a compound annual growth rate of 12.2% [2]. This growing volume of video data generated by these systems demands an efficient and reliable method for anomaly detection, which remains a challenging task due to the dynamic and complex nature of video content. Traditional approaches often rely on manual monitoring or primitive automated techniques, which are time consuming and prone to errors and inefficiencies. Since abnormal activities are rare compared to normal ones, identifying and detecting anomalies is more complicated than other video analysis forms because of the data imbalances between normal and abnormal segments. This has increased interest in leveraging advanced machine learning methods, particularly deep learning, to automate and improve anomaly detection accuracy in video surveillance. Video anomaly detection involves identifying events or patterns in video data that deviate from the norm. These abnormalities might be anything from identifying safety risks in industrial settings to strange behaviors in crowded areas. The critical challenge in video anomaly identification is the wide range of behaviors that are considered normal in various contexts but anomalies in others; for instance, running is a typical activity for people as part of an exercise routine but running inside a bank could be perceived as anomalous or suspicious behavior. Moreover, anomalous events are complicated in nature, are often rare, and vary greatly in appearance and nature. Traditional machine learning and deep learning methods are the two main categories of anomaly detection approaches. Traditional machine learning techniques have shown impressive results in many video anomaly areas, such as abnormal human action recognition, by capturing shallow features from video data [3]. These techniques included support vector machines (SVMs) [4], Markov models [5], Bayesian networks [6], random forests (RFs) [7], probabilistic-based models [8], sparse reconstruction [9], histograms of optical flows (HOFs) [10], and histograms of oriented gradients (HOGs) features [11; 12]. On the other hand, they mostly rely on handcrafted features and preprocessing, which take a lot of time and resources to complete. According to [PERSON] et al. [13], they perform poorly in real-world scenarios and do not scale well for various datasets. The use of deep learning techniques introduced novel strategies that surpass conventional approaches and tackle the drawbacks related to traditional machine learning [14; 15; 16]. We do not address traditional machine learning techniques in this work; instead, we concentrate on the most recent advancements in deep learning models. Recent improvements in deep learning have produced effective techniques for analyzing videos, which have significantly improved anomaly identification. These techniques, in particular, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated remarkable potential in terms of capturing the temporal dynamics and spatial characteristics of videos. Nevertheless, they typically require an extensive amount of labeled data and have trouble capturing the nuances and variability of anomalies, which can result in errors like missed detections and false positives.