ArticleSensors (Basel, Switzerland)2021
Detecting Animal Contacts-A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- StaticPigDetv2: Performance Improvement of Unseen Pig Monitoring Environment Using Depth-Based Background and Facility Information.Sensors (Basel, Switzerland) · 2026Article
- Detection and Continuous Tracking of Breeding Pigs with Ear Tag Loss: A Dual-View Synergistic Method.Animals : an open access journal from MDPI · 2025Article
- An Automatic Ear Temperature Monitoring Method for Group-Housed Pigs Adopting Infrared Thermography.Animals : an open access journal from MDPI · 2025Article
- Precision Livestock Farming Applied to Swine Farms-A Systematic Literature Review.Animals : an open access journal from MDPI · 2025Review
- A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis.Sensors (Basel, Switzerland) · 2025Article
- Multistage pig identification using a sequential ear tag detection pipeline.Scientific reports · 2025Article
- Impact of proximity definitions and sampling rates on social networks in pigs based on tracking using computer vision.Scientific reports · 2025Article
- Toward the automatic detection of social interactions in gestating sows using image analysis data.Journal of animal science · 2025Article
- Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare.Sensors (Basel, Switzerland) · 2024Article
- Towards automatic farrowing monitoring-A Noisy Student approach for improving detection performance of newborn piglets.PloS one · 2024Article
- Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model.Sensors (Basel, Switzerland) · 2023Article
- Detection of Pig Movement and Aggression Using Deep Learning Approaches.Animals : an open access journal from MDPI · 2023Article
- Sensors and Robotics for Digital Agriculture.Sensors (Basel, Switzerland) · 2023Article
- A Method for Obtaining 3D Point Cloud Data by Combining 2D Image Segmentation and Depth Information of Pigs.Animals : an open access journal from MDPI · 2023Article
- YOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid Network.Sensors (Basel, Switzerland) · 2023Article
- TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network.Sensors (Basel, Switzerland) · 2023Article
- The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming.Sensors (Basel, Switzerland) · 2022Review
- Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions.Sensors (Basel, Switzerland) · 2022Review
- Study on Poultry Pose Estimation Based on Multi-Parts Detection.Animals : an open access journal from MDPI · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of social interactions is of fundamental importance for animal behavioral studies, addressing numerous problems like investigating the influence of social hierarchical structures or the drivers of agonistic behavioral disorders. However, the majority of previous studies often rely on manual determination of the number and types of social encounters by direct observation which requires a large amount of personnel and economical efforts. To overcome this limitation and increase research efficiency and, thus, contribute to animal welfare in the long term, we propose in this study a framework for the automated identification of social contacts. In this framework, we apply a convolutional neural network (CNN) to detect the location and orientation of pigs within a video and track their movement trajectories over a period of time using a Kalman filter (KF) algorithm. Based on the tracking information, we automatically identify social contacts in the form of head-head and head-tail contacts. Moreover, by using the individual animal IDs, we construct a network of social contacts as the final output. We evaluated the performance of our framework based on two distinct test sets for pig detection and tracking. Consequently, we achieved a Sensitivity, Precision, and F1-score of 94.2%, 95.4%, and 95.1%, respectively, and a MOTA score of 94.4%. The findings of this study demonstrate the effectiveness of our keypoint-based tracking-by-detection strategy and can be applied to enhance animal monitoring systems.
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