Biodiv Sci ›› 2026, Vol. 34 ›› Issue (4): 25287.  DOI: 10.17520/biods.2025287  cstr: 32101.14.biods.2025287

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Wildlife pose estimation method based on the SCD-HRNet Model: A case study of the Saihanwula Region, Inner Mongolia

Ziyi Kong1,2,3, Degang Wang1,2,3, Jiantao Wang4, Zhiyong Pei5, Jing Sun6, Changchun Zhang1,2,3*, Junguo Zhang1,2,3*   

  1. 1 College of Engineering, Beijing Forestry University, Beijing 100083, China 

    2 National Key Laboratory of Efficient Production of Forest Resources, Beijing 100083, China 

    3 Research Center for Intelligent Biodiversity Monitoring, Beijing Forestry University, Beijing 100083, China 

    4 Administration of Ulanba National Nature Reserve, Chifeng, Inner Mongolia 025450, China 

    5 College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018, China 

    6 Administration of Wulanhe Local Nature Reserve, Ulanhot, Inner Mongolia 137400, China

  • Received:2025-07-20 Revised:2025-09-30 Accepted:2025-11-07 Online:2026-04-20
  • Contact: Changchun Zhang, Junguo Zhang
  • Supported by:
    High-Level Talent Recruitment Program – Junguo Zhang(陕西省科学院科技计划项目(2025K-32)); Research on Incremental Learning Mechanisms and Methods for Wildlife Monitoring Images in Open Environments(国家自然科学基金项目(32371874)); Research on Generalization Mechanisms and Methods for Open-Environment Wildlife Monitoring Images in Beijing(北京市自然科学基金项目(6244053)); Open Set Domain Adaptation Recognition Mechanisms and Methods for Wetland Waterfowl Monitoring Images(32401569)

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Abstract:

Aims: The conservation of wild animals in the Saihanwula region of Inner Mongolia is of great significance for maintaining regional biodiversity. Behavioral analysis helps enhance the scientific basis and intelligent management of biodiversity conservation, with pose estimation serving as the prerequisite and core support for behavioral analysis. 

Methods: Aiming at the problem that the accuracy of pose estimation is decreased due to illumination changes, high-speed movement of animals and complex environmental occlusion factors in wildlife monitoring, in this paper, we propose a novel wildlife pose estimation method combining attention mechanism and dynamic confidence suppression (selective coordinate-enhanced decoupling-HRNet, SCD-HRNet). Firstly, combined with the squeeze-and-excitation (SE) module, the channel-level context features were extracted by global average pooling to enhance the discrimination ability of the network for species morphological features and effectively solve the problem of feature distortion caused by illumination changes. Secondly, in order to deal with the positioning deviation caused by the high-speed movement of animals, the coordinate attention (CA) mechanism is introduced to decompose the two-dimensional coordinates into the horizontal and vertical components for sinusoidal position coding, and the bidirectional attention mechanism is used to establish the cross-direction long-range dependence relationship to improve the joint positioning accuracy under motion blur. Finally, the dynamic confidence suppression (DCS) module is proposed to establish an adaptive threshold function based on the model inference accuracy to realize the robust detection of the key points in occlusion. 

Results: This paper carries out comparative experiments to verify the performance of the model. The experimental results show that the mean average precision of the SCD-HRNet method reaches 82.61% and 69.79% on the collected and labeled wild animal dataset in Saihanwula area and the AP-10K public animal dataset, respectively, which are better than the existing methods. 

Conclusion: The proposed SCD-HRNet method significantly improves the pose estimation accuracy of wildlife images in complex ecological scenes, and provides reliable technical support for wildlife behavior analysis in ecological monitoring.

Key words: wild animals, pose estimation, HRNet, SE attention mechanism, coordinate attention mechanism, dynamic confidence suppression

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