ArticleSensors (Basel, Switzerland)2023
TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Computer Vision Applied to the Analysis of Pig Behavior Patterns in an Air-Conditioned Environment.Animals : an open access journal from MDPI · 2026Article
- Detecting Dairy Cattle Protective Behaviors via a Multi-Stage Attention SlowFast Network.Animals : an open access journal from MDPI · 2026Article
- MMBs_TransNeXt: a hierarchical adaptive feature learning model for pig behavior recognition in precision livestock farming.Frontiers in veterinary science · 2026Article
- Integrating Deep Learning and Transcriptomics to Assess Livestock Aggression: A Scoping Review.Biology · 2025Review
- Recognition of Foal Nursing Behavior Based on an Improved RT-DETR Model.Animals : an open access journal from MDPI · 2025Article
- S_TransNeXtM: a pig behavior recognition model based on the TransNeXtM and the sLSTM.Frontiers in veterinary science · 2025Article
- Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach.Animals : an open access journal from MDPI · 2024Article
- Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model.Sensors (Basel, Switzerland) · 2023Article
- A Short Video Classification Framework Based on Cross-Modal Fusion.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often time-consuming and labor-intensive, while deep learning models with a large number of parameters can result in slow training times and low efficiency. To address these issues, this paper proposes a novel deep mutual learning enhanced two-stream pig behavior recognition approach. The proposed model consists of two mutual learning networks, which include the red-green-blue color model (RGB) and flow streams. Additionally, each branch contains two student networks that learn collaboratively to effectively achieve robust and rich appearance or motion features, ultimately leading to improved recognition performance of pig behaviors. Finally, the results of RGB and flow branches are weighted and fused to further improve the performance of pig behavior recognition. Experimental results demonstrate the effectiveness of the proposed model, which achieves state-of-the-art recognition performance with an accuracy of 96.52%, surpassing other models by 2.71%.
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Registered trials
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.