Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning

Author:

Fuentes Alvaro12ORCID,Han Shujie12ORCID,Nasir Muhammad Fahad12,Park Jongbin12,Yoon Sook3ORCID,Park Dong Sun12ORCID

Affiliation:

1. Department of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea

2. Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Republic of Korea

3. Department of Computer Engineering, Mokpo National University, Muan 58554, Republic of Korea

Abstract

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.

Funder

Ministry of Education

Ministry of Agriculture, Food, and Rural Affairs

Ministry of Science and ICT (MSIT), Rural Development Administration

National Research Foundation of Korea

Publisher

MDPI AG

Subject

General Veterinary,Animal Science and Zoology

Reference32 articles.

1. Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare;Schillings;Front. Anim. Sci.,2021

2. Exploring the Susceptibility of Smart Farming: Identified Opportunities and Challenges;Jerhamre;Smart Agr. Technol.,2022

3. Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want;Dawkins;Front. Anim. Sci.,2021

4. Review: Management of Livestock Behavior to Improve Welfare and Production;Orihuela;Animal,2021

5. Farm Animal Cognition-Linking Behavior, Welfare and Ethics;Nawroth;Front. Vet. Sci.,2019

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3