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IHPP: Improved Human Parts as Points for Multi-Person Pose Regression
Hao Xu1,2, Yihan Liu1, Yuting Fan1
1Computer Science and Technology, Lishui University, Lishui 323000, China.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
IHPP improves multi-person pose estimation by enhancing directional context and part-wise regression, achieving high accuracy efficiently. This method refines single-stage approaches for better human action understanding in various applications.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Multi-person pose estimation is crucial for intelligent sensing systems in surveillance, healthcare, and sports analytics.
- Existing two-stage methods are inefficient due to complex pipelines, while single-stage methods struggle with long-range keypoint localization.
Purpose of the Study:
- To introduce IHPP, a novel single-stage multi-person pose estimation algorithm.
- To enhance efficiency and accuracy by addressing limitations in receptive field and feature interference.
Main Methods:
- IHPP refines AdaptivePose by incorporating directional context modeling and a part-wise regression branch.
- The algorithm is presented in three sizes (IHPP-S, IHPP-M, IHPP-L) with optimized channel dimensions for varying efficiency-accuracy trade-offs.
Main Results:
- IHPP-L achieves 72.3 AP at 28 fps on COCO, outperforming existing models.
- IHPP-M offers 69.0 AP at 35 fps, and IHPP-S runs at 42 fps with 9.4M parameters.
- On CrowdPose, IHPP-M surpasses AdaptivePose-W48 with fewer parameters.
Conclusions:
- IHPP effectively improves multi-person pose estimation accuracy and efficiency.
- The directional context and part-wise regression design contribute to superior performance on benchmark datasets.
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