## 5 Conclusions  
In recent years, video surveillance systems have been used in various public areas, such as hospitals, airports, bus terminals, and university campuses, to ensure public safety. Closed circuit television (CCTV) systems allow the recorded images to be transferred to consecutive monitors and monitored by expert personnel. It takes a long time to detect abnormal situations by monitoring videos from multiple monitors at the same time, and situations that need to be detected may escape the attention of the staff. For this reason, smart systems have begun to be used to automate video inspections. In this study, a fast, reliable, and accurate abandoned object detection method for video surveillance is  
Figure 15: Proposed method precision comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Dataset** & **Method** & \(p\) **(\%)** & \(r\) **(\%)** & \(f\)1 **(\%)** \\ \hline
[15] & ABODA & Dual background model & & & \\ YOLO-NAS & 75.00 & 75.00 & 75.00 \\
[18] & ABODA & Background subtraction & 35.41 & 70.83 & - \\
[19] & Self-collected dataset & YOLOv4 + Kalman Filter (KF) & 79.00 & 86.00 & 83.00 \\
[20] & i-Lids AVSS-AB data & YOLOv3 & 95.50 & 56.40 & - \\ Our Method & ABODA & Keyframe detection, YOLOv8l & 97.90 & 97.00 & 97.40 \\  & & + Deep SORT & 97.90 & 97.00 & 97.40 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Literature method comparison.  
presented using YOLOv8 and DeepSort. The same scenes can be found consecutively in continuously recorded videos. Using repeated frames in model training may result in memorization in the model and also increases the hardware cost. Therefore, in this study, deep transfer learning-based keyframe detection was first performed to remove repeated frames in the videos. Human and object classes in videos trained with the YOLOv8 deep neural network were detected using the extracted keyframes. Detected human and object classes were tracked in consecutive frames with the DeepSort algorithm and the distance between them was measured to detect abandoned objects. When compared with other methods proposed in the relevant field, it has been observed that the method proposed in this study can perform real-time detection with fast and high accuracy rates. A new method has been developed for the detection of abandoned objects, created with state-of-the-art technologies, robust even in unexpected situations, adaptive under different conditions, versatile enough to perform different functions, and scalable for datasets of different sizes. Future work will focus on the hybrid use of different deep learning architectures on larger datasets, especially those containing crowded images, such as sports competitions.  
Software, M.Y.; Validation, M.K.; Writing--original draft, M.Y.; Writing--review & editing, M.Y. and M.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the TUBITAK (The Scientific and Technological Research Council of Turkey) under Grant No: 5220154. This study was supported by the Firat University Scientific Research Projects Support Program (FUBAP) under Grant No: MF.24.18.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Thanks to [PERSON] for his contributions.  
The authors declare no conflict of interest.