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Simple Summary: Over 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. Cattle 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. [ABSTRACT FROM AUTHOR]

Simple Summary: Over the years, the identification of individual cattle has assumed a pivotal role in health monitoring, reproduction management, behavioral research, and performance tracking. In this study, we propose a method based on artificial intelligence for identifying known and new (unknown) individual Hanwoo cattle, a native breed of Korea, by utilizing cattle's face images. To accomplish this, we strategically positioned a network of CCTV cameras within a closed farm, demonstrating the efficacy of non-intrusive sensors in capturing real-world data. Furthermore, we devised open-set techniques to tackle challenges such as varying illumination, overlapping objects, and fluctuations in cattle's face orientations. Our research method not only demonstrated excellent recognition performance in complex real-world cattle's datasets, but can also be applied to open-set scenarios, wherein unmarked or new cattle may join the herd. Our proposed method can be readily adapted to identifying various livestock species, offering real-time individual recognition, which yields valuable insights for farm management. This deep learning approach amplifies the efficiency of farm operations, thus playing a pivotal role in advancing the agriculture industry as a whole. Accurate identification of individual cattle is of paramount importance in precision livestock farming, enabling the monitoring of cattle behavior, disease prevention, and enhanced animal welfare. Unlike human faces, the faces of most Hanwoo cattle, a native breed of Korea, exhibit significant similarities and have the same body color, posing a substantial challenge in accurately distinguishing between individual cattle. In this study, we sought to extend the closed-set scope (only including identifying known individuals) to a more-adaptable open-set recognition scenario (identifying both known and unknown individuals) termed Cattle's Face Open-Set Recognition (CFOSR). By integrating open-set techniques to enhance the closed-set accuracy, the proposed method simultaneously addresses the open-set scenario. In CFOSR, the objective is to develop a trained model capable of accurately identifying known individuals, while effectively handling unknown or novel individuals, even in cases where the model has been trained solely on known individuals. To address this challenge, we propose a novel approach that integrates Adversarial Reciprocal Points Learning (ARPL), a state-of-the-art open-set recognition method, with the effectiveness of Additive Margin Softmax loss (AM-Softmax). ARPL was leveraged to mitigate the overlap between spaces of known and unknown or unregistered cattle. At the same time, AM-Softmax was chosen over the conventional Cross-Entropy loss (CE) to classify known individuals. The empirical results obtained from a real-world dataset demonstrated the effectiveness of the ARPL and AM-Softmax techniques in achieving both intra-class compactness and inter-class separability. Notably, the results of the open-set recognition and closed-set recognition validated the superior performance of our proposed method compared to existing algorithms. To be more precise, our method achieved an AUROC of 91.84 and an OSCR of 87.85 in the context of open-set recognition on a complex dataset. Simultaneously, it demonstrated an accuracy of 94.46 for closed-set recognition. We believe that our study provides a novel vision to improve the classification accuracy of the closed set. Simultaneously, it holds the potential to significantly contribute to herd monitoring and inventory management, especially in scenarios involving the presence of unknown or novel cattle. [ABSTRACT FROM AUTHOR]