## 1 Introduction  
The rapid increase in the global population has resulted in an increased demand for beef [1]. Figure 1, published by the Organization for Economic Co-operation and Development (OECD), shows beef consumption per capita by OECD countries during 2014-2021 [2], wherein it is evident that beef consumption has been increasing annually owing to the rising demand. Therefore, precision livestock farming (PLF) is actively being developed to ensure effective and efficient livestock production [3]. PLF involves the development of monitoring systems for various livestock characteristics, including health and welfare [4]. Analyzing livestock behavior in PLF is crucial because it enables the assessment of the current conditions of animals [5]. Detecting the behavior of livestock not only aids in the real-time monitoring of animal health but also plays a crucial role in farm management. The ability to detect changes in behavior can act as an early indicator of potential health issues, enabling timely intervention and treatment. Additionally, it can assist in quickly identifying and addressing any stressors or discomorts experienced by the livestock. In particular, observing behaviors such as drinking, eating, and lying down can help determine the health status of livestock or diagnose diseases early [6; 7]. However, monitoring livestock behavior requires substantial human resources and labor. Furthermore, the direct observation of livestock is unsustainable, as the observer's attention inevitably decreases because of fatigue. Therefore, recent studies have focused on methods that enable observing livestock behavior without the need for continuous human monitoring. [PERSON] et al. [8] collected cow behavioral data through a neck-mounted three-axis accelerometer sensor and a jaw-mounted pressure sensor. Based on these data, they classified cow behaviors into three categories: ruminating, feeding, and others. Their classification model employed a convolutional neural network architecture and achieved an F1 score of 0.82, which indicates the good overall accuracy of the model. Similarly, [PERSON] et al. [9] classified \"defecation\" and \"urination\" behaviors using an accelerometer attached to the cows' tails. They developed a classification model using the random forest algorithm, which is an ensemble algorithm that makes predictions by learning from numerous decision trees, and achieved _sensitivity (recall)_ and _precision_ scores of \(>\)86.7%. Methods that analyze cattle behavior based on sensor data are effective and exhibit high performance for livestock behavior classification. However, such sensor-based data collection methods necessitate the direct attachment of sensors to livestock, which can cause stress in the animals. Additionally, these sensors are susceptible to damage and contamination; therefore, farmers must periodically check them, which increases their workload [10; 11]. In contrast to research based on sensor-based methods, some studies have determined livestock behavior using data obtained from 3D cameras. [PERSON] et al. [12] introduced an algorithm that uses the Intel RealSense depth camera and a support vector machine to detect aggressive behavior in pigs; the algorithm exhibited an accuracy of 97.5%. As noted in previous studies, employing 3D camera data under suitable conditions can result in high performance. However, 3D cameras are susceptible to direct sunlight, which hinders their outdoor use. Additionally, their range is limited as they can only capture data effectively within a specific range [13]. Moreover, 3D cameras have lower resolutions and cost more than 2D RGB cameras, rendering their usage on livestock farms challenging [14]. Therefore, some studies have analyzed livestock behavior using 2D image data. [PERSON] et al. [15] proposed an algorithm that detects the behaviors of sows using image data and classifies them into \"drinking\", \"urination\", and \"mounting\" using the MobileNet model; the algorithm exhibited a _mean average precision (mAP)_ of 93.4%.