ArticleAnimals : an open access journal from MDPI2024
Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack.
Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Underwater Multi-Target Tracking and Behavioral Rhythm Analysis of Chinese Giant Salamander Based on TransTrack-OC-SORT.Animals : an open access journal from MDPI · 2026Article
- Enhancing Pig Behavior Recognition in Complex Environments: A Transfer Learning-Assisted YOLO11 Network with Wavelet Convolution and Synergistic Attention.Animals : an open access journal from MDPI · 2026Article
- Design and Verification of a Non-Contact Body Dimension Measurement System for Jiangquan Black Pigs Based on Dual-View Depth Vision.Animals : an open access journal from MDPI · 2025Article
- GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity.Animals : an open access journal from MDPI · 2025Article
- Body Temperature Detection of Group-Housed Pigs Based on the Pairing of Left and Right Ear Roots in Thermal Images.Animals : an open access journal from MDPI · 2025Article
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Authors and funding
6 authors.
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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 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.
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