mlsanityData to 14 Oct 2026

Improvement of human health lifespan with hybrid group pose estimation methods

arXiv 2506.03169 ↗First posted 28 May 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Human beings rely heavily on estimation of poses in order to access their body movements. Human pose estimation methods take advantage of computer vision advances in order to track human body movements in real life applications. This comes from videos which are recorded through available devices. These para-digms provide potential to make human movement measurement more accessible to users. The consumers of pose estimation movements believe that human poses content tend to supplement available videos. This has increased pose estimation software usage to estimate human poses. In order to address this problem, we develop hybrid-ensemble-based group pose estimation method to improve human health. This proposed hybrid-ensemble-based group pose estimation method aims to detect multi-person poses using modified group pose estimation and modified real time pose estimation. This ensemble allows fusion of performance of stated methods in real time. The input poses from images are fed into individual meth-ods. The pose transformation method helps to identify relevant features for en-semble to perform training effectively. After this, customized pre-trained hybrid ensemble is trained on public benchmarked datasets which is being evaluated through test datasets. The effectiveness and viability of proposed method is estab-lished based on comparative analysis of group pose estimation methods and ex-periments conducted on benchmarked datasets. It provides best optimized results in real-time pose estimation. It makes pose estimation method more robust to oc-clusion and improves dense regression accuracy. These results have affirmed po-tential application of this method in several real-time situations with improvement in human health life span

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 38 references that are in the map. First posted on arXiv 28 May 2025.

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30 of 38 references.

References · 30

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18 more
  1. 2021Conditional DETR for Fast Training Convergencecited by 424
  2. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  3. 2016RMPE: Regional Multi-person Pose Estimationcited by 340
  4. 2019HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimationcited by 247
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  7. 2023RTMPose: Real-Time Multi-Person Pose Estimation based on MMPosecited by 175
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  17. 2022Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Datacited by 2
  18. 2024Efficient Human Pose Estimation: Leveraging Advanced Techniques with MediaPipecited by 1

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