Evidence map›Paper›PMID 37370530›Full record

ArticleAnimals : an open access journal from MDPI2023

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

Alvaro Fuentes, Shujie Han, Muhammad Fahad Nasir, Jongbin Park, Sook Yoon, Dong Sun Park

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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  17. Recognition of Foal Nursing Behavior Based on an Improved RT-DETR Model.Animals : an open access journal from MDPI · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Alvaro FuentesDepartment of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.ORCID 0000-0001-8847-1541
Shujie HanDepartment of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.ORCID 0000-0002-8830-3553
Muhammad Fahad NasirDepartment of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Jongbin ParkDepartment of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Sook YoonDepartment of Computer Engineering, Mokpo National University, Muan 58554, Republic of Korea.ORCID 0000-0002-0194-3356
Dong Sun ParkDepartment of Electronics Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.ORCID 0009-0000-1256-0214

Funding

Ministry of Agriculture, Food and Rural Affairs 421044-04National Research Foundation of Korea 2019R1A6A1A09031717National Research Foundation of Korea 2020R1A2C2013060
6 · The paper itself

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.

Indexed as

animal welfarecattle behaviordeep learningindoor farmprecision livestock farmingvideo

Identifiers

PMID37370530
PMCPMC10295180

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.