## 2 Related Work  
The detection of suspicious/abandoned objects in public areas plays an important role in public safety. For this reason, intelligent systems, such as deep learning and machine learning, have recently been used to automatically and quickly detect suspicious situations in places constantly monitored by CCTV cameras. This section includes a review of the state-of-the-art methods proposed for the detection of abandoned objects. [PERSON] et al. [19] proposed a new method for removing suspicious objects in the foreground based on background subtraction. They categorized whether the foreground objects were static or moving by comparing their center of gravity coordinates. If the foreground object is stationary for a predefined period of time and its size is also within the predefined range, it is classified as an unattended/abandoned object. Testing their proposed method on the ABODA dataset, the researchers measured the Correct Object Detection Rate (CODR), Object Success Rate (OSR), and False Alarm Rate (FAR) as 70.83%, 67.25%, and 35.41%, respectively. [PERSON], [PERSON] [20] proposed a new alternative method for suspicious object detection systems that have become mandatory for public safety. In the system they proposed, they detected six different types of objects, namely a person, backpack, handbag, book, umbrella, and suitcase, using the YOLOv4 algorithm. They tracked the detected objects using the Kalman Filter (KF). [PERSON] et al. [21] proposed a suspicious object detection system consisting of four stages. They defined the method steps as object detection, object tracking, owner analysis, and abandoned object analysis. They detected two classes, human and object, using the YOLOv3 neural network. They used time and distance information to determine the owner of the detected objects. They defined objects that were over a certain distance and moved away for more than 10 s as abandoned objects. They showed that they could detect abandoned objects and their owners with 65.66% accuracy. [PERSON] et al. [22] proposed a three-stage method, the block diagram of which is presented in Figure 1, which integrates advanced learning algorithms and background masks to improve the accuracy of automatic abandoned object detection in public areas. In the first stage, data preprocessing was performed to remove noise from the images. In the second stage, abandoned object recognition (AOR) was performed to distinguish between dynamic and static entities. In the third and final stage, feature correlation analysis was performed to detect abandoned objects. [PERSON] et al. [23] proposed a new method for detecting abandoned objects in public areas and identifying people who left the object at the scene. Firstly, they improved the image quality of videos by using residual dense networks. Abandoned objects were identified by measuring the distance between them and people. In order to reduce the False Alarm Rate, a fuzzy rule-based threat assessment module was used in the method. As an alternative to the abandoned object type, [PERSON] et al. [24] proposed a YOLOv3-based method for detecting abandoned grocery carts in parking lots after shopping. They showed that their proposed method could detect abandoned grocery carts in parking lots with a mAP value of 93.00%. [PERSON] et al. [25] proposed a suspicious object detection method using PETS2006, ABODA and their own datasets with a binary background model. They improved their proposed method according to different situations, such as sudden illumination changes, long-term abandonment, and the owner retaking the object. The distance between the fixed foreground objects and their owners is measured, and if it is outside the default distance, an abandoned object alarm is sounded. For the detection of abandoned objects, it is possible to augment the methods based on background subtraction [26] and deep learning and machine learning [27; 28; 29; 30] in the literature.