MambaPose: A Human Pose Estimation Method
Author Information
Author(s): Zhang Jianqiang, Hou Jing, He Qiusheng, Yuan Zhengwei, Xue Hao
Primary Institution: Taiyuan University of Science and Technology
Hypothesis
Can the Mamba-based human pose estimation method improve accuracy in detecting keypoints in complex scenes?
Conclusion
The proposed MambaPose method significantly improves the accuracy of human pose estimation, especially in dense crowds and for small targets.
Supporting Evidence
- The MambaPose algorithm improved AP50 by 1.1% compared to typical algorithms.
- Experimental results showed that the proposed method effectively detects keypoints in complex scenes.
- The model outperformed existing methods in both subjective and objective evaluations.
Takeaway
This study created a new way to find where people's body parts are in pictures, making it easier to see them even when there are many people close together.
Methodology
The study used a GMamba structure for feature extraction, slice downsampling for resolution reduction, and an adaptive threshold focus loss to enhance keypoint detection.
Limitations
The model may face challenges in real-time performance and computational resource limitations due to a large number of parameters.
Digital Object Identifier (DOI)
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