## 1 Introduction  
Closed circuit television (CCTVs) systems consist of multiple security cameras connected to one or more video monitors [1]. Such security systems are used in many public areas, such as schools, hospitals, and airports, to detect various abnormal behaviors, suspicious objects, and movements. Video surveillance systems (VSSs) that record 24 h a day, 365 days a year are widely used today. In such systems, video inspections are performed by human operators. It is not possible to examine multiple video frames simultaneously with a high attention level. This inevitably leads to the problem of a decreasing attention level as operator fatigue increases [2, 3]. Visual surveillance involves the practice of identifying, classifying, and detecting normal and abnormal human activities. Walking, running, waving, or talking are considered normal human movements. Explosive attacks, fights, running crowds, theft, and abandoned objects (bags, suitcases, etc.) are considered abnormal human movements. Especially, in order to prevent explosive attacks, the detection of abandoned/suspicious objects is of great importance for human security [4]. Various methods are suggested for the fastest and most accurate anomaly detection [5]. Nowadays, with the spread of security and monitoring systems in many areas, video data have also grown rapidly. The increasing success rates of intelligent systems and models, such as machine learning and deep learning, on visual data have enabled image analysis and video analysis to be performed with automatic systems. In this way, video and image analyses that require a long time and effort can be performed in a shorter time and with higher accuracy [6]. In a video analysis, each frame of the video is treated as an independent image. Thus, each frame is processed separately to detect objects, behaviors, and situations from the video [7]. Applications such as object detection and suspect detection in video analysis include the classification of objects or samples within a frame and the determination of their locations. Object tracking is the continuous tracking of the detected object or sample by assigning a number to it in consecutive frames [8]. Detection, classification, segmentation, and tracking of various situations, events, and people found in video data are carried out using various image and video processing techniques. However, deep learning-based techniques are mostly used in the field of video and image processing because they have a high success rate and a self-learning structure [9; 10]. Deep learning consists of modeling artificial neural networks to work like the human brain. It is defined as a system that can automatically extract features from large amounts of data and learns on its own with minimal human effort. There are various deep learning networks, such as the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Deep Generative Networks. The areas using deep learning are basically as follows [11; 12]. * Autonomous vehicles;
* Voice and video processing;
* Natural language processing;
* Handwritten character recognition;
* Medical image processing;
* Signature verification;
* Big data. Suspicious/abandoned object detection is important for ensuring public safety, preventing potential dangers and taking precautions against security breaches. The dual background model [13] is used to extract static foregrounds [14], and deep learning-based [15; 16] methods are generally used to detect abandoned objects. The traditionally used binary background model for abandoned/suspicious object detection and background model for extracting static foregrounds have disadvantages such as staticity, misleading results against illumination change, shadow area problems, and the difficulty of accurate detection in complex backgrounds.