A Lightweight Top-Down Multi-Person Pose Estimation Method Based on Symmetric Transformation and Global Matching

Li, Yangyang; Yang, Danqing; Chen, Yanqiao; Cheng, Peng; Sun, Zhiguo; Jiao, Licheng

doi:10.1109/access.2022.3151136

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…To improve coordinate estimation performance, both the human detection and detected human pose estimation parts need refinement. Geometric and spatial transformation processes using STN (Spatial Transformation Network) and SDTN (Spatial Detransformer Network) were suggested in [12]. This network [12] extracts highquality human candidate frames and shows features that improve recognition performance.…”

Section: Pose Estimation Researchmentioning

confidence: 99%

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GolfMate: Enhanced Golf Swing Analysis Tool through Pose Refinement Network and Explainable Golf Swing Embedding for Self-Training

Ju,

Kim,

Lee

2023

Applied Sciences

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Digital fitness has become a widely used tool for remote exercise guidance, leveraging artificial intelligence to analyze exercise videos and support self-training. This paper introduces a method for self-training in golf, a sport where automated posture analysis can significantly reduce the costs associated with professional coaching. Our system utilizes a pose refinement methodology and an explainable golf swing embedding for analyzing the swing motions of learners and professional golfers. By leveraging sequential coordinate information, we detect biased pose joints and refine the 2D and 3D human pose estimation results. Furthermore, we propose a swing embedding method that considers geometric information extracted from the swing pose. This approach enables not only the comparison of the similarity between two golf swing poses but also the visualization of different points, providing learners with specific and intuitive feedback on areas that require correction. Our experimental results demonstrate the effectiveness of our swing guide system in identifying specific body points that need adjustment to align more closely with a professional golfer’s swing. This research contributes to the digital fitness domain by enhancing the accuracy of posture analysis and providing a specialized and interpretable golf swing analysis system. Our proposed system offers a low-cost and time-efficient approach for users who wish to improve their golf swing, paving the way for broader applications of digital fitness technologies in self-training contexts.

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Section: Pose Estimation Researchmentioning

confidence: 99%

“…Geometric and spatial transformation processes using STN (Spatial Transformation Network) and SDTN (Spatial Detransformer Network) were suggested in [12]. This network [12] extracts highquality human candidate frames and shows features that improve recognition performance. Moreover, ref.…”

Section: Pose Estimation Researchmentioning

confidence: 99%

GolfMate: Enhanced Golf Swing Analysis Tool through Pose Refinement Network and Explainable Golf Swing Embedding for Self-Training

Ju,

Kim,

Lee

2023

Applied Sciences

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“…In [27], Li et al, Describe a straightforward technique for estimating the pose of multiple people based on symmetric transition and global matching. The best matching approach for the human body-key points graph is built using the global matching method and the KM algorithm.…”

Section: B Multi Person Pose Estimationmentioning

confidence: 99%

Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model

et al. 2023

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Human pose estimation has drawn extensive attention recently and there has been significant progress on it due to the rising popularity of convolutional neural networks (CNN). However, existing stateof-the-art approaches suffer from occlusion, complicated backgrounds, and substantial position fluctuations because of disregarding the human body form. Human parsing is a very pertinent activity that can provide crucial semantic data about bodily parts for position estimation. To overcome the aforesaid limitations, this paper introduces a human pose estimation method using a group-based convolutional neural network model. The proposed method adopts a bottom-up parsing strategy that yields features to extract skeletal key points in the human body. Moreover, it creates a grouping of anatomical key points for individuals by utilizing the non-parametric description for the key point association vector field. Experimental results indicate that the proposed method provides superior performance than the state-of-the-art algorithms in terms of accuracy. In addition, it optimizes its output and detects occluded as well as invisible key points by incorporating feature representation. The proposed method surpasses the recent methods, achieving 93% of the mean average accuracy.

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An OpenPose-based System for Evaluating Rehabilitation Actions in Parkinson's Disease

Huang

Chou

Lee

2022

2022 International Automatic Control Conference (CACS)

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A Lightweight Top-Down Multi-Person Pose Estimation Method Based on Symmetric Transformation and Global Matching

Cited by 5 publications

References 47 publications

GolfMate: Enhanced Golf Swing Analysis Tool through Pose Refinement Network and Explainable Golf Swing Embedding for Self-Training

GolfMate: Enhanced Golf Swing Analysis Tool through Pose Refinement Network and Explainable Golf Swing Embedding for Self-Training

Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model

An OpenPose-based System for Evaluating Rehabilitation Actions in Parkinson's Disease

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