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An Animal Pose Estimation Method for Handling Keypoint Occlusion

Xiaoying Zhu, Yaning Jiang
Frontiers in Science and Engineering, (2025), Vol.5, No.11, pp.55-65
Published: November 24, 2025
DOI: 10.54691/hp63xf14
PDF: Download Full Text PDF
Abstract

Animal pose estimation plays a vital role in various fields such as animal behavior analysis and wildlife conservation. However, in real-world scenarios, keypoint occlusion frequently occurs, which limits the model’s ability to accurately localize keypoints. To address this issue, this paper proposes an occlusion-aware animal pose estimation algorithm based on an improved HRNet. The proposed method incorporates a data augmentation strategy driven by strongly connected keypoints to enrich the diversity of occluded keypoints in the training set and enhance the model’s inference capability for invisible keypoints. In addition, a dynamic upsampling module integrating both channel and spatial attention mechanisms is designed to improve the restoration quality of fine-grained features during the upsampling process. Furthermore, a progressive feature fusion strategy is introduced to reduce the information loss caused by large-scale upsampling in multi-scale feature integration, thereby further enhancing the fusion performance. Experimental results on the public AP-10K dataset demonstrate that the proposed method significantly outperforms the original HRNet and other comparison algorithms in terms of accuracy.

Keywords: Animal Pose Estimation, Keypoint Occlusion, Data Augmentation, Dynamic Upsampling.
APA Citation: Xiaoying Zhu, Yaning Jiang (2025). An Animal Pose Estimation Method for Handling Keypoint Occlusion. Frontiers in Science and Engineering, 5(11), 55-65. https://doi.org/10.54691/hp63xf14

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