## 3 Proposed Approach
### Abandoned Object Detection  
Large language models have started to be used in many areas with the development of transformer architecture. In order to obtain the desired outputs from large language models, correct inputs must be provided. This situation has led to the development of prompt engineering techniques, such as zero-shot learning, one-shot learning, few-shot learning, instruction-based prompting, and chain of thought. These techniques are used to produce customized prompts, such as learning without examples, learning with a single example, learning from more than two examples, learning through instructions, and solving by thinking step by step. In this study, the instruction-based prompting technique was used to teach the language model how to perform certain instructions [40; 41; 42]. In order to determine whether the detected object was suspicious or abandoned, the duration of the object being in the same location and the Euclidean distance were taken into account. Then, LLM outputs strengthened with prompt engineering were created for expert personnel information. For this purpose, first of all, the location information of the detected human and object classes was recorded in a *.txt file. Since the videos were recorded at 15 FPS, approximately 15 frames represent 1 s. In order to determine the frame that remained motionless in the same position for 30 s, the coordinates of the object should not change in the 450 consecutive frames. For this, it was first checked whether the object remained stationary for 450 frames, as mathematically shown in Equation (10).  
\[C_{t}=\left\{\begin{aligned} C_{t-1}+1,&\text{if }|x_{t}-x_{t-1}|=0\text{ and }|y_{t}-y_{t-1}|=0\\ 0,&\text{ other situations}\end{aligned}\right. \tag{10}\]The distance between the human classes labeled with 1 and the object classes labeled with 0 for each frame was calculated using the Euclidean distance formula presented in Equation (11). k (cm/pixel) is the scale factor. It is used to convert the calculated distance in pixels to the real-world distance.  
\[\text{d}=\left(\sqrt{\left(\text{x}_{2}-\text{x}_{1}\right)^{2}+\left(\text{y}_ {2}-\text{y}_{1}\right)^{2}}\right)\times k \tag{11}\]  
Objects whose distance was measured greater than the specified threshold value (50 cm) and remained motionless in the same position for 30 s were marked as abandoned objects. The coordinates of the human and object classes in each 450-frame result were added to the task section of the prompt created with the instruction-based prompting technique to create the explanation. Thus, LLM-based explanation outputs were created for expert personnel. The pseudo-code of the Python library that produces the abandoned object detection output by taking the output video of the DeepSORT algorithm and the txt file containing the class coordinate information in the frames as the input is presented in Algorithm 2. The abandoned object detection flowchart is given in Figure 5.  
```
Input: DeepSort Output Video Class Coordinate Information txt folder Output: Abandoned Object while f < 450 Time calculation if i x(t) - x(t - t) 1 = 0 & ly(t) - y(t - 1) 1 = 0 f = f + 1 else f = 0 while i < d Measure distance between person and object d = (sqrt((x2 - x1)^{2}+(y2-y1)^{2}2)) \(\times\) k if distance (d) > 50 then Object = Abandoned Object else uptade new frame i = i + 1
```  
**Algorithm 2**Abandoned Object Detection  
Figure 5: Abandoned object detection flowchart.