Mathematical Modeling and Computer Vision in Animal Activity or Behavior: 2nd Edition

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 567

Special Issue Editors


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Guest Editor
College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: precision livestock farming; computer vision; behavior detection and analysis; animal tracking; animal welfare

E-Mail Website
Guest Editor
College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: precision livestock farming; computer vision; behavior detection and analysis; animal tracking; animal welfare
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Special Issue Information

Dear Colleagues,

The Special Issue "Mathematical Modeling and Computer Vision in Animal Activity or Behavior: 2nd Edition" of the journal Animals focuses on the application of advanced computational techniques to the study of animal behavior and activity. This Special Issue highlights the use of mathematical modeling and computer vision technologies to understand, monitor, and predict various aspects of animal behavior in a range of species, ranging from domestic animals to wildlife.

The scope of the Special Issue encompasses the development and implementation of algorithms and models that analyze animals’ movements, behavior patterns, and interactions within their environment. It encourages submissions that apply techniques such as machine learning, image processing, and sensor-based systems to capture and interpret behavioral data in an automated and objective manner. These approaches allow for real-time monitoring and detailed analysis that can improve animal welfare, conservation, and management.

This Special Issue requests contributions that explore novel computational methods, interdisciplinary approaches, and case studies demonstrating practical applications in fields such as ethology, ecology, animal husbandry, and veterinary science. By advancing the technological frontiers of behavioral studies, this Special Issue will offer valuable insights into animal biology, improve welfare standards, and optimize productivity in agricultural and conservation settings.

Dr. Haiming Gan
Prof. Dr. Yueju Xue
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • animal behavior
  • mathematical modeling
  • computer vision
  • machine learning
  • behavior prediction
  • image processing
  • automated monitoring
  • sensor-based systems
  • animal welfare
  • livestock farming

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Published Papers (1 paper)

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Research

16 pages, 6692 KiB  
Article
Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack
by Shuqin Tu, Haoxuan Ou, Liang Mao, Jiaying Du, Yuefei Cao and Weidian Chen
Animals 2024, 14(22), 3299; https://doi.org/10.3390/ani14223299 - 16 Nov 2024
Viewed by 319
Abstract
Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of [...] Read more.
Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of group-reared pigs to detect health problems and improve animal welfare promptly. We have developed the method named Pig-ByteTrack. Our approach addresses target detection, Multi-Object Tracking (MOT), and behavioral time computation for each pig. The YOLOX-X detection model is employed for pig detection and behavior recognition, followed by Pig-ByteTrack for tracking behavioral information. In 1 min videos, the Pig-ByteTrack algorithm achieved Higher Order Tracking Accuracy (HOTA) of 72.9%, Multi-Object Tracking Accuracy (MOTA) of 91.7%, identification F1 Score (IDF1) of 89.0%, and ID switches (IDs) of 41. Compared with ByteTrack and TransTrack, the Pig-ByteTrack achieved significant improvements in HOTA, IDF1, MOTA, and IDs. In 10 min videos, the Pig-ByteTrack achieved the results with 59.3% of HOTA, 89.6% of MOTA, 53.0% of IDF1, and 198 of IDs, respectively. Experiments on video datasets demonstrate the method’s efficacy in behavior recognition and tracking, offering technical support for health and welfare monitoring of pig herds. Full article
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