## 3 Proposed Approach  
In the proposed study, the open source The ABandone Object DAtaset (ABODA) was used for abandoned object detection. the ABODA dataset is an open source dataset that includes 11 real-world scenes created for abandoned object detection. The dataset includes both crowded and less-crowded scenes. Detailed environment information of the videos is presented in Table 2. The 11 videos were first separated into frames. After the frames were passed through the keyframe stage, they were labeled in the YOLO format. For YOLOv8, 70% of the dataset was used for training and 30% for testing. Within the scope of the proposed method, all training and tests were performed on Google Colab with NVIDIA Tesla T4 GPU 16 GB GDDR6 Memory, Intel(R) Xeon(R) Central Processing Unit (CPU) @ 2.00 GHz hardware infrastructure. NVIDIA is headquartered in Santa Clara, California, USA, and Google is headquartered in Mountain View, California, USA. The ABandoned Object DAtaset (ABODA) dataset consists of 11 videos created in various indoor and outdoor locations for the purpose of detecting abandoned objects. Each of the videos, which is approximately 2-3 min long, has a size of 720 \(\times\) 480. The implementation steps of the proposed method, the block diagram of which is presented in Figure 2, are shown below and in Algorithm 1. * Keyframes based on deep transfer learning were detected in the dataset. * The YOLOv8 deep neural network was used in different sub-architectures to detect people and objects, such as bags, suitcases, etc., in the videos. * The weights of the YOLOv81 model with the highest performance were recorded to be used as the input in the DeepSort algorithm. * The object and human classes detected using the Deepsort object tracking algorithm were tracked. The ID and coordinate information of the human and object classes were recorded in a *,txt file for each frame. \begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Dataset** & **Method** \\ \hline
[19] & ABODA Dataset & Background subtraction method \\
[20] & Private Dataset & YOLOv4 \\
[22] & KISA Dataset. ABODA Dataset & Background Matting with Dense ASPP \\
[25] & PETS2006, ABODA, Private Dataset & Dual background model \\
[31] & PETS2006 and AVSS2007, ABODA Dataset & Short- and long-term background models \\
[32] & IIITP20 Dataset, PETS06 Dataset, ABODA Dataset, & Convolutional Neural Network (CNN) \\
[33] & CAVIAR Dataset & SVM classifier \\
[33] & iLIDS and ISLab Dataset & SVM classifier \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Abandoned object detection literature comparison. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Video** & **Recorded Location** & **Lighting** \\ \hline
1 & Indoor & Normal indoor illumination \\
2 & Outdoor & Daylight \\
3 & Outdoor & Daylight \\
4 & Outdoor & Daylight \\
5 & Outdoor & Night \\
6 & Outdoor & Night illumination \\
7 & Indoor & Normal indoor illumination \\
8 & Indoor & Contrast illumination \\
9 & Indoor & Normal indoor illumination \\
10 & Indoor & Normal indoor illumination \\
11 & Indoor (Crowded) & Normal indoor illumination \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ABODA dataset description. To detect abandoned objects, the duration of the object staying in the same place in consecutive frames was determined. Then, the distance between the human and the object class was measured using the coordinate information in the *.txt file. ```
Step 1: Keyframe Detection keyframes = detect_keyframes(dataset) Step 2: Object and Human Detection with YOLOv8 for each frame in video: Object and human detection is done with YOLOv8 detections = YOLOv8.detect(frame) Classes of objects: People, bags, suitcases etc.