Abstract
With applications in animal monitoring, veterinary diagnostics, behavioral analysis, and robotics, real-time estimation 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 environmental contexts and runs for fifteen seconds. Keypoints marking important body joints for all types of animals were added to the extracted frames. Using the YOLOv8n posture architecture - which provides a balanced trade-off between speed and accuracy - we performed 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 information. 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 experimental 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-Pose for the estimation of animal posture based on key points. Future 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#