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Related Experiment Video

Updated: Apr 15, 2026

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CDO-POSE: A Lightweight Model for 2D Human Pose Estimation.

Haifeng Xu1, Jingke Chen1, Shuhan Cai1

  • 1School of Intelligent Systems Science and Engineering, Jinan University, Qianshan Street, Zhuhai 519070, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces CDO-POSE, a lightweight human pose estimation method. It achieves high accuracy with fewer parameters and real-time performance on edge devices.

Keywords:
YOLOv11human pose estimationlightweight model

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Human pose estimation (HPE) faces challenges in balancing accuracy and efficiency.
  • Real-time and resource-constrained scenarios require optimized HPE methods.

Purpose of the Study:

  • To develop a lightweight and efficient human pose estimation method.
  • To improve feature representation and upsampling accuracy for precise keypoint localization.

Main Methods:

  • Introduced the Context Anchor Attention (CAA) module for enhanced feature representation.
  • Incorporated Dynamic Sampling (DySample) to improve upsampling precision.
  • Utilized Object Keypoint Similarity Loss (OKS-Loss) for discriminative keypoint evaluation.

Main Results:

  • The CDO-POSE model achieves comparable accuracy to YOLOv11s-pose with significantly fewer parameters.
  • Achieved real-time inference speeds (39.79 FPS at 480p, 29.23 FPS at 720p) on NVIDIA Jetson Orin Nano.
  • Demonstrated suitability for deployment on edge devices.

Conclusions:

  • CDO-POSE offers an effective solution for efficient and accurate human pose estimation.
  • The proposed method is well-suited for real-time applications on edge computing platforms.