## 1 Introduction
Deep learning-based methods are often preferred because they provide a good performance on image data, although they require preprocessing, such as data labeling, data augmentation when necessary, and the selection of the appropriate deep learning model [17]. Although deep learning-based methods require a large amount of data for model training, the presence of almost identical data together causes overfitting in the model. Overfitting causes the model to memorize the results and produce successful results only on those data [18]. Many studies have been conducted on abandoned object detection. However, due to the neglect of environmental differences and the limited real-time applications, it has been determined that false negatives/positives occur in real-life applications. In this study, modern artificial intelligence techniques, such as YOLOv8, DeepSORT, Generative Artificial Intelligence (GenAI), and keyframe detection, were used to overcome these difficulties. The use of these techniques allowed a faster and more accurate detection of abandoned objects. In general, by detecting keyframes used in this study, efficiency was increased by processing only important frames. YOLOv8, which meets the need for fast and secure intervention, was used to detect object and human classes. By tracking object and human classes with DeepSORT, the problem of inadequacy in the feature extraction was solved and tracking uncertainties in consecutive frames was prevented. Distance measurement and explanation outputs between human and object classes were created via GenAI with prompt engineering techniques. The main contribution point that distinguishes the proposed method in this study from other studies is the integrated use of artificial intelligence-based systems in all steps. In addition, the applicability of the model is increased with LLM-based explanations and the explanations are made understandable. The advantages of the method proposed within the scope of this study over the existing methods are presented below. * This study focuses on providing a new and versatile method for abandoned object detection. * Real-time, fast, and high-accuracy abandoned object detection is achieved with the integrated use of deep learning-based models. * The use of YOLOv8, DeepSORT, and GenAI makes abandoned object detection more accurate and faster, while keyframe detection increases the efficiency by ensuring that only important frames are processed. * The applicability of the model is supported by the result explanation module, which is suitable for improvement with user feedback based on the large language model. * It is a pioneering approach to perform abandoned object detection in public areas in real time and with a low hardware cost. * The created system will be used in subsequent studies as a module of the Video Analytics Artificial Intelligence library. The remaining sections of the paper are structured as follows: Section 2 includes a comparative review of the proposed works in the literature on abandoned object detection. Section 3 provides an overview of the proposed method. Section 4 includes the experimental results of the proposed method and its comparison with state-of-the-art methods. Section 5 includes a general evaluation of the paper.