Citation

Bhuiyan, Md Roman and Yesilova, Barbaros and Shadakh, Anastasia and Arunesh, Shrish and Napolitano, Giulio and Islam, Md Baharul and Abdullah, Junaidi (2025) Real-Time Animal Pose Estimation Using Computer Vision Techniques. In: 2025 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 29-31 October 2025, Ras Al Khaimah, United Arab Emirates.

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Abstract

With applications in animal monitoring, veterinary diagnostics, behavioral analysis, and robotics, real-time estima tion of animal posture is an area of increasing interest in computer vision. In this work, we propose a method based on deep learning approaches to assess animal positions in real time. Selected for their applicability in both home and agricultural settings, the study centres on four animal classes: chicken, dog, horse, and cow. An important output of this work is a bespoke dataset created especially for posture estimation activities, including annotated videos. Every video in the dataset records continuous movement under different lighting and en vironmental contexts and runs for fifteen seconds. Keypoints marking important body joints for all types of animals were added to the extracted frames. Anastasia Shadakh Faculty of CSI BSBI Berlin, Germany q1037777@students.berlinsbi.com Md Baharul Islam Department of CSE Florida Gulf Coast University Florida, USA mislam@fgcu.edu Pose for the estimation of animal posture based on key points. Fu ture directions include growing the data set, increasing keypoint accuracy, and including temporal consistency across frames. The data set is available at this link https://drive.google.com/drive/ folders/1xci52bt9IxcYQrq36r2fQBaLvx3cSGHH# Index Terms—Smart farming, computer vision, deep learning, YOLOv8n, animal detection and monitoring. I. INTRODUCTION Using the YOLOv8n posture architecture- which provides a balanced trade-off between speed and accuracy- we per formed posture estimation. Although YOLO models are usually optimized for object recognition, we fine-tuned YOLOv8n-Pose to predict both bounding boxes and body keypoints, therefore enabling the real-time identification of intricate postural in formation. Trained on an annotated dataset using supervised learning, the model was tested on another test set from the same distribution. The proposed model achieves a PosePR mAP at the IoU threshold 0.5 of 99.5% in all classes, according to the experimen tal data. The dog class showed lower precision and F1 scores; the dog and horse classes showed decreased recall. The model maintains strong performance in real-time even if interclass posture variability and occlusion in video frames present natural difficulties. The system handles video input at an average frame rate enough for monitoring systems to be live. This study emphasizes the need for custom data sets tailored to real-world activities and the viability of employing YOLOv8n-ture directions include growing the data set, increasing keypoint accuracy, and including temporal consistency across frames. The data set is available at this link https://drive.google.com/drive/ folders/1xci52bt9IxcYQrq36r2fQBaLvx3cSGHH#

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	mart farming, computer vision, deep learning, YOLOv8n, animal detection and monitoring.
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	18 Mar 2026 00:00
Last Modified:	18 Mar 2026 00:00
URII:	http://shdl.mmu.edu.my/id/eprint/15508

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