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An Animal Pose Estimation Method for Handling Keypoint Occlusion

Xiaoying Zhu, Yaning Jiang
Vol. 5, No. 11, pp. 55-65, 2025 DOI: 10.54691/hp63xf14 November 24, 2025
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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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