## 4 Experimental Results
### Object Tracking and Abandoned Object Detection Result
The output images of the proposed model are presented in Figure 14. Other methods proposed in the relevant field in the literature were examined and the comparison of the method proposed in this study with other methods is presented in Table 9 and Figure 15. It was seen that there were diversities such as different lighting conditions, causing outdoor and indoor images in the datasets used for abandoned object detection to be similar to real-world scenes. While using different types of data contributes to the learning scope of the model in deep learning algorithms, it has been observed that it can cause incorrect definitions in methods based only on background and foreground extractions. In this article, transfer learning-based keyframe detection was implemented to make the dataset most suitable for training and to prevent duplicate data. Then, YOLOv8-based human and object detection with a fast and effective object detection potential was realized. The tracking of objects in all consecutive frames was performed with the Deep SORT algorithm and the determination of abandoned objects was achieved. With these advantages, a more effective abandoned object detection method was presented in terms of software and hardware efficiency compared to similar methods in the literature. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Contribution** & **Contribution Rate** \\ \hline New & New in terms of integrating advanced deep learning technologies for object detection and tracking. & The method supported by new transformer-based models is an innovative study in the field with a confidence rate of over 97\%. \\ \hline Robust & In cases where objects are partially visible in consecutive frames or disappear and become visible again, DeepSORT can be used to track them when they become visible again. Robust due to its ability to continue its function even in such unexpected situations. & Our model can detect abandoned objects with a 97.4\% f1-score confidence rate. \\ \hline Adaptive & Adaptive due to providing high detection rates on images obtained under different lighting and climate conditions. & \
\begin{tabular}{c} Our model can adapt to 15\% data changes at a rate of 90\%. \\ \end{tabular} \\ \hline Versatile & Versatile due to its capacity to perform multiple phased functions, such as keyframe detection, detection of human/object classes, tracking of human/object classes, and abandoned object detection. & Our model is versatile with a minimum f1-score of 92\% in indoor, outdoor, and different lighting conditions. \\ \hline Scalable & Scalable due to its applicability on datasets of different sizes than those used experimentally in the study. &
\begin{tabular}{c} Although the calculation time increases as the video size increases, the keyframe detection rate remains limited to 20\% at most. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Contributions of the proposed method to the literature. Figure 13: Ablation experiments results. Figure 14: Proposed method outputs. In addition, the proposed work is compared with various studies in terms of accuracy, speed, and computational efficiency. The USD (Unknown Sensitive Detector) [43] model can detect unknown objects with high accuracy, especially with SAM integration. However, it requires high GPU usage and a long processing time in large visual models. Practical Abandoned Object Detection [22] reduces the false positive rate with background blurring and a dense ASSP, but causes heavy computational delays in real-time applications. The Real-Time Deep Learning Method [44] is a fast and real-time method due to the use of a CNN. However, accuracy losses are experienced compared to new state-of-the-art techniques. In this study, keyframe detection increases speed and efficiency by preventing unnecessary frames from being processed. YOLOv8 provides fast and high-accuracy object detection. DeepSORT reduces false positives by providing real-time object tracking. The LLM-based explanation provides meaningful explanations of detected objects.