## 1 Introduction  
Measuring the number of people in indoor environments plays a pivotal role in preventing the spread of infectious diseases like COVID-19 and avoiding safety accidents. The existing research on people counting primarily utilizes image-processing techniques that acquire images through cameras and analyze head counts from the obtained images to estimate the number of people [1, 2]. Since cameras can visually capture real-life situations, they can provide data for people to easily understand and analyze. In addition, camera-based systems are able to determine the number of people by leveraging high-resolution images with the latest deep learning and computer vision algorithms. However, camera-based people-counting systems present the following issues, which limits the system capabilities and their widespread use. First, capturing images by using cameras involves directly recording individuals' faces and bodies, which leads to the privacy concerns and potential legal issues in certain countries [3]. Second, the accuracy of camera-based systems are highly dependent on environmental conditions. Low lighting, strong backlighting, and shadows can degrade the quality of images captured by cameras, which makes accurate IOD difficult. Additionally, when people are overlapped or obscuredby objects and when blind spots occur, the accuracy of counting people decreases. Furthermore, installing and maintaining high-quality cameras and the related infrastructure can be costly. To address these issues, wireless sensing systems have attracted a lot of attention. By analyzing the varying patterns of wireless signals, such as Infrared, UWB (Ultra Wide-Band), and Wi-Fi in a surveillance region, they detect and interpret the changes in the region. All of these technologies enable sensing even in low-light conditions. However, compared to Wi-Fi signals, Infrared and UWB have shorter sensing ranges and their signals can be blocked by obstacles. Thus, the performance and the utility of wireless sensing systems using Infrared and UWB are limited. In addition, Wi-Fi networks are pervasive in homes, offices, and public spaces. This ubiquity makes Wi-Fi sensing a highly accessible, scalable, and cost-effective solution for people counting. RSS (Received Signal Strength) and CSI (Channel State Information) are two representative Wi-Fi signal data that have been utilized for Wi-Fi sensing. Since signal strength decreases as the distance between a sender and a receiver increases, RSS has been primarily used for positioning techniques in early Wi-Fi sensing research [4]. However, RSS only provides general information about the measurement environment and is susceptible to random variations in the environment [5]. Recently, CSI has been used for Wi-Fi sensing because it provides more detailed information about the wireless signal propagation environment [6]. Thus, in this paper, we use CSI as Wi-Fi signal data for people counting. On the other hand, deep learning models have been advanced remarkably. These models significantly enhance the accuracy of classification and regression problems. Consequently, to perceive environments in a device-free manner, research is actively underway to analyze Wi-Fi signals measured in various environments using deep learning techniques [7; 8]. More specifically, to adopt a deep learning model, the people-counting problem by using Wi-Fi signals is often regarded as a classification problem. Each distinct count of people in a monitored area is treated as a separate class. Then, a deep learning model is trained to learn the classification boundaries among the classes to categorize the number of people in the area. Various deep learning models have been used for Wi-Fi sensing, which includes MLP (Multilayer Perceptron), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and more recently, transformer models. Each of these architectures has its strengths and is chosen based on the specific requirements of the sensing task.