Simple SummaryOver the years, the monitoring of cattle behavior has been recognized as an essential aspect of ensuring their health and welfare. In this study, we propose a framework based on artificial intelligence for monitoring the behavior of individual cattle through action recognition and tracking over time. Our research focuses specifically on studying the behavior of Hanwoo cattle, a native breed of Korea. To achieve this, we deployed a network of CCTV (closed-circuit television) cameras strategically placed within a closed farm, showcasing the effectiveness of non-intrusive sensors in capturing real-world information. Furthermore, we devised techniques to tackle challenges such as occlusion, size variations, and motion deformation. Our proposed technology represents a significant advancement in the field of precision livestock farming. By enabling the monitoring of individual animal behavior over time, it offers valuable insights that optimize the management of farms. This innovative approach enhances the efficiency and effectiveness of farm operations, ultimately contributing to the overall success and progress of the agriculture industry.AbstractCattle behavior recognition is essential for monitoring their health and welfare. Existing techniques for behavior recognition in closed barns typically rely on direct observation to detect changes using wearable devices or surveillance cameras. While promising progress has been made in this field, monitoring individual cattle, especially those with similar visual characteristics, remains challenging due to numerous factors such as occlusion, scale variations, and pose changes. Accurate and consistent individual identification over time is therefore essential to overcome these challenges. To address this issue, this paper introduces an approach for multiview monitoring of individual cattle behavior based on action recognition using video data. The proposed system takes an image sequence as input and utilizes a detector to identify hierarchical actions categorized as part and individual actions. These regions of interest are then inputted into a tracking and identification mechanism, enabling the system to continuously track each individual in the scene and assign them a unique identification number. By implementing this approach, cattle behavior is continuously monitored, and statistical analysis is conducted to assess changes in behavior in the time domain. The effectiveness of the proposed framework is demonstrated through quantitative and qualitative experimental results obtained from our Hanwoo cattle video database. Overall, this study tackles the challenges encountered in real farm indoor scenarios, capturing spatiotemporal information and enabling automatic recognition of cattle behavior for precision livestock farming.