Earticle

초록

영어

This research develops a smart livestock monitoring system leveraging artificial intelligence with YOLOv8 and OC-SORT technologies to precisely monitor and analyze cow behavior, enhancing detection and tracking capabilities in complex environments. It delves into cows' movement speed and acceleration to uncover behavior patterns and health status, focusing on estrus-related behaviors for optimal breeding strategies. The study identifies changes in activity, social interactions, and mating behaviors as crucial estrus indicators, contributing significantly to livestock management innovations. By offering methods for visual behavior analysis representation, it simplifies the interpretation of findings, advancing livestock monitoring technology. This work not only contributes to smarter livestock management by providing an AI-driven cow behavior tracking model but also opens new avenues for research and efficiency improvements in the field.

목차

Abstract
1. Introduction
2. Theoretical Basis
2.1 OC-SORT Object Tracking Algorithm
3. Materials and methods
3.1 Data Collection
3.2 Data Annotation
3.3 Data Preprocessing
4. Experiment Results
4.1 Model Training and evaluation environment
4.2 Experimental data structure
4.3 Object detection performance Evaluation
4.4 Object Tracking Performance Evaluation
4.5 Performance evaluation of behavior analysis
5. CONCLUSION
Acknowledgement
References

키워드

저자

• Doyoon Jung [ Master student., Department of Computer Engineering, Honam University, Korea ]
• Sukhun Kim [ Ph.D. student, Department of Computer Engineering, Honam University, Korea ]
• Namho Kim [ Ph.D., Associate Professor, Department of Computer Engineering, Honam University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380