Knowledge-Enhanced Deep Learning for Identity-Preserved Multi-Camera Cattle Tracking

Shujie Han, Alvaro Fuentes, Jiaqi Liu, Zihan Du, Jongbin Park, Jucheng Yang, Yongchae Jeong, Sook Yoon () and Dong Sun Park ()
Additional contact information
Shujie Han: Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Alvaro Fuentes: Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Jiaqi Liu: Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Zihan Du: College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China
Jongbin Park: Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Jucheng Yang: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541000, China
Yongchae Jeong: Division of Electronics and Information Engineering, IT Convergence Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea
Sook Yoon: Department of Computer Engineering, Mokpo National University, Muan 58554, Republic of Korea
Dong Sun Park: Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea

Agriculture, 2025, vol. 15, issue 18, 1-22

Abstract: Accurate long-term tracking of individual cattle is essential for precision livestock farming but remains challenging due to occlusions, posture variability, and identity drift in free-range environments. We propose a multi-camera tracking framework that combines bird’s-eye-view (BEV) trajectory matching with cattle face recognition to ensure identity preservation across long video sequences. A large-scale dataset was collected from five synchronized 4K cameras in a commercial barn, capturing both full-body movements and frontal facial views. The system employs center point detection and BEV projection for cross-view trajectory association, while periodic face recognition during feeding refreshes identity assignments and corrects errors. Evaluations on a two-day dataset of more than 600,000 images demonstrate robust performance, with an AssPr of 84.481% and a LocA score of 78.836%. The framework outperforms baseline trajectory matching methods, maintaining identity consistency under dense crowding and noisy labels. These results demonstrate a practical and scalable solution for automated cattle monitoring, advancing data-driven livestock management and welfare.

Keywords: multi-camera tracking; bird’s-eye-view projection; face recognition; cross-view identity association; large-scale imperfect datasets (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/18/1970/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/18/1970/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:18:p:1970-:d:1752529

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().