# AI-Based Smart Monitoring Framework for Livestock Farms
## 2 Related Work  
Animals express their condition and responses to external environmental stimuli through their behavior. Most livestock facilities are equipped with CCTV, and by collecting video footage to analyze livestock behavior patterns, it is possible to detect abnormal or irregular behaviors in real time. A rapid response to such behaviors enables precise livestock management, improving overall efficiency and animal welfare [2; 3; 4]. In the early stages of smart farming, accelerometers and gyroscopic sensors were attached to livestock to observe their movements. However, this approach caused discomfort to the animals while failing to yield positive results. As ICT technologies and livestock industry innovations have progressed, more advanced integration has enabled the collection of information about livestock conditions and barn environments. Utilizing these data, research is actively being conducted on applications such as growth measurement, animal condition recognition, individual behavior classification, animal welfare assessment, and disease detection. In 2018, [PERSON] and colleagues conducted research utilizing visible light and thermal imaging cameras installed on the ceiling of livestock barns to recognize livestock behavior. However, traditional image processing techniques had limitations in fully identifying animal behaviors. Subsequently, simple object-tracking research evolved into more advanced motion recognition studies using deep learning-based CNN feature extractors, enabling the recognition of complex movements [5; 6; 7; 8]. These techniques have since been applied to areas such as facial recognition and basic behavior classification. Additionally, research has been conducted on utilizing CCTV footage not only to monitor livestock behavior but also to integrate spatiotemporal metadata. This approach aims to notify farm managers of specific events in real time, enhancing farm management efficiency. Another study proposed a method to convert a hierarchical structure into domain-specific information by structuring it as graph nodes. By mapping spatiotemporal data and behavioral recognition--such as individual and group behaviors--along with graph-structured interrelated information, an identification number was assigned to each simple action level [6; 7]. This allowed for the simultaneous processing of each object's behavior based on body parts, simple actions, and identification numbers. The method was applied to monitor both the individual and group behaviors of Hanwoo cattle, enabling object tracking through spatiotemporal analysis. However, notwithstanding the depth and sophistication of these studies, practical implementation in actual Hanwoo livestock farms remains challenging. High costs and the need for additional ICT equipment pose significant barriers, making it difficult to effectively improve animal welfare conditions in current livestock environments [8]. Recent studies in real-time object detection have prominently focused on CNN-based single-stage algorithms such as the YOLO (You Only Look Once) series, which have demonstrated outstanding performance in real-time object detection tasks over the past few years. The YOLO series includes both anchor-based and anchor-free approaches [9; 10; 11; 12; 13]. Since its introduction, the YOLO approach has undergone continuous development, significantly improving its performance, and it has become synonymous with real-time object detection. YOLOv3 further addressed the limitations of previous versions, especially in detecting small objects more effectively [5; 6; 7; 8]. Research has been actively conducted using YOLO-based models to detect and monitor cattle behavior and body parts. A study was conducted to automatically identify livestock using YOLOv5, and the detection accuracy was improved by using the mosaic augmentation technique for partial images such as the cow's mouth [2]. A study was conducted to identify the feeding behavior of dairy cows.