## 4 Experimental Results
### Object Tracking and Abandoned Object Detection Result  
The detection of abandoned objects that may be dangerous for public safety in public areas is an important aspect in video surveillance. Therefore, in this study, a new method based on YOLOv8 + DeepSORT is proposed to detect abandoned/suspicious objects. The human and object classes detected with YOLOv8 are tracked with the Deep SORT algorithm. An identification number is assigned to each class tracked in consecutive frames. The location information of the detected human and object classes was saved in a *.txt file, as seen in Figure 10b. Then, objects that are in the same location for 450 consecutive frames and the distance between the human and object class is greater than 50 cm are labeled as abandoned objects. The height of the people in the images was determined as 175 cm to provide a real-world calibration. The 720 \(\times\) 480 image pixels were compared to the pixels covered by the human class to create a scale factor, k, in cm/pixel. The measured distances were multiplied by the scale factor to obtain the real-world distance. The total suspicious object detection success criteria from the videos were measured as 97.8% precision, 96.8% recall, and 97.4% f1-score. The success rates of the model for each video are presented in Table 7. With the development of the transformer architecture, large language models have shown great success in the fields of text generation, understanding, and interpretation [40; 41]. In this study, generative artificial intelligence (GenAI)-based explanations were created for frames in order to improve user experiences. For this purpose, the prompt seen in Figure 11 was provided as an input to the GPT-3.5-Turbo large language model. As the output, information about the human and object classes on the frame was created. In addition, information about whether the object was abandoned was also generated. The distribution of TP, FP, and FN values obtained from the model according to the number of frames in each video is presented in the graph in Figure 12. The success of the description text produced by LLM was measured by calculating the commonly used BLEU,  
Figure 9: YOLOv8-based person and object detection model outputs. ROUGE, and METEOR metrics. The metrics explained below were automatically calculated using the nltk, rouge-score, and sacrebleu libraries in Python ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/))). BLEU: Calculated to determine how similar the text produced by the model is to human-generated texts. ROUGE: Calculated to determine how similar the text produced with the reference text in the field of text generation is in terms of meaning. METEOR: Calculated to evaluate the success of the model in language generation by considering synonyms and word order. In this study, successful explanations were generated with 78% BLEU, 83% ROUGE, and 89% METEOR scores from LLM enhanced with the instruction-based prompt engineering technique. By creating an LLM-based explanation, the usability of the system was made clear and understandable. The features of the method proposed in this study for the detection of abandoned objects, its contributions to the relevant field in the literature, and its contribution rates are presented in Table 8.  
The main components that directly affect the model's performance are keyframe detection, object detection with YOLOv8, and object tracking with DeepSORT. Therefore, ablation experiments were performed to find the component that affected the model's performance the most among these three components. The experimental results are presented in the graph in Figure 13. Accordingly, it was seen that object detection with YOLOv8 increased the model accuracy the most, followed by object tracking with DeepSORT. Figure 11: Prompt and sample output generated for LLM. Figure 12: Abandoned object detection TP, FP, and FN value comparison.