## 1 Introduction
High computational power also poses a significant challenge, requiring a significant resource investment [4]. Over the past two decades, a significant amount of research has been conducted on image and video processing to overcome these challenges. These studies have focused on developing new methods for anomaly detection that are more efficient and effective while also addressing the challenges associated with intelligent anomaly detection. Overall, understanding the issues of traditional anomaly detection methods and exploring new methods are crucial for the continued advancement of video surveillance. This survey aimed to comprehensively examine the existing literature on Artificial Intelligence (AI) techniques for detecting abnormal events in surveillance videos. Specifically, the survey aimed to provide an overview of the most-commonly used datasets and evaluate their benefits and drawbacks. Additionally, the survey highlights key difficulties in the literature, providing insight into areas that require further research and development. First, it is important to note that the use of AI in surveillance video analysis has gained significant attention in recent years due to its potential to improve the effectiveness and efficiency of surveillance systems. This is particularly relevant in security and surveillance, where detecting abnormal real-time events is crucial for public safety. Various approaches and techniques, based on evolving artificial intelligence methods, enable the analysis of surveillance videos and the identification of abnormal events such as suspicious behavior, criminal activities, and other potential threats. The contributions of our study are as follows:* A comprehensive survey of state-of-the-art AI approaches for SVAD was conducted. This analysis thoroughly examined the current research in the field, highlighting the most-popular techniques and methodologies used in SVAD. * The commonly used datasets, needs, and issues of SVAD were explored in depth. This examination provides insight into the challenges faced by researchers in this field and the specific requirements of the datasets used for SVAD. * The trade-offs in SVAD are discussed from the viewpoint of the performance of approaches that use AI techniques. This analysis provides a nuanced understanding of the trade-offs between performance and other factors, such as computational complexity and scalability. * Areas of application, challenges, and possible future work in the field of AI for SVAD are presented. This examination provides a comprehensive overview of the potential applications of SVAD and the challenges that must be overcome to realize AI's potential in this field. The remainder of this study is organized as follows: Section 2 presents a review of the related literature in the field of SVAD. Section 3 conducts an in-depth examination of AI techniques employed in SVAD. Section 4 provides an analysis of the commonly used datasets in this field. Section 5 evaluates the performance of existing SVAD applications. Section 6 engages in a critical discourse on the findings and implications, and finally, Section 7 presents our conclusions and recommendations for future research.