Abstract The work proposes an improved method for assessing the motion stability of badminton athletes using advanced image segmentation techniques. It highlights the growing importance of sports stability in performance enhancement and scientific training. Traditional video segmentation methods are noted as insufficient for accurate athlete segmentation, prompting the integration of deep learning with fuzzy neural networks. The authors present a hybrid algorithm combining the computational... The work proposes an improved method for assessing the motion stability of badminton athletes using advanced image segmentation techniques. It highlights the growing importance of sports stability in performance enhancement and scientific training. Traditional video segmentation methods are noted as insufficient for accurate athlete segmentation, prompting the integration of deep learning with fuzzy neural networks. The authors present a hybrid algorithm combining the computational efficiency of neural networks with the precision of fuzzy logic to better handle illumination changes and motion dynamics in video frames. Experimental results demonstrate that their genetic fuzzy neural network algorithm achieves significantly lower error rates 5.01% and 2.52% compared to traditional and standard fuzzy neural approaches, which often exceed 15% error. The method shows high accuracy in segmenting moving targets, such as badminton players, enabling clear foreground background differentiation and reliable feature extraction. Performance tests confirm improvements in both processing speed and segmentation quality, especially for complex motion scenarios. Despite these advances, the study acknowledges a limitation: it does not address the methodology for movement step analysis. Overall, the research contributes a feasible, accurate, and efficient image segmentation solution tailored for sports stability analysis, leveraging adaptive deep learning to support real world athletic performance evaluation and decision making. Read More Read Less

Abstract This paper proposes an improved image segmentation model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance computer vision performance. Traditional K-means suffers from sensitivity to initial cluster centers and high computational complexity, especially in RGB color space. To address these issues, the authors integrate PSO to perform a global search for optimal initial cluster centers before applying K-means for... This paper proposes an improved image segmentation model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance computer vision performance. Traditional K-means suffers from sensitivity to initial cluster centers and high computational complexity, especially in RGB color space. To address these issues, the authors integrate PSO to perform a global search for optimal initial cluster centers before applying K-means for local refinement. This hybrid approach avoids local optima, reduces computational load, and accelerates processing speed. Experiments compare the proposed model against standard K-means and PSO-K models using three color images. Results show the enhanced model achieves the shortest segmentation runtime across all test images and delivers superior edge detail and segmentation accuracy. The study highlights that while image complexity affects processing time, the proposed method maintains consistently high efficiency and precision. The authors conclude that their model significantly improves image segmentation in computer vision tasks, making it more suitable for real world applications in fields like agriculture, healthcare, and smart systems. By leveraging color space conversion and intelligent optimization, the model demonstrates robustness, faster convergence, and better handling of fine image details compared to existing techniques. Read More Read Less

Abstract The paper proposes an integrated approach for analyzing human movements using fuzzy clustering combined with deep learning techniques. It addresses challenges in traditional computer vision methods such as occlusion, motion blur, and lighting variations that hinder accurate player tracking and posture recognition in dynamic match environments. The proposed methodology involves preprocessing video frames to reduce noise, followed by fuzzy clustering based image segmentation to... The paper proposes an integrated approach for analyzing human movements using fuzzy clustering combined with deep learning techniques. It addresses challenges in traditional computer vision methods such as occlusion, motion blur, and lighting variations that hinder accurate player tracking and posture recognition in dynamic match environments. The proposed methodology involves preprocessing video frames to reduce noise, followed by fuzzy clustering based image segmentation to distinguish players from the background. Deep learning is then applied for feature extraction, posture identification, and motion tracking. The fuzzy Cmeans (FCM) algorithm plays a central role, offering soft clustering that accommodates ambiguous boundaries and overlapping actions. The paper reviews related work, highlights FCM's strengths and limitations, and introduces enhancements, including residual-driven FCM and contextual clustering, to improve noise robustness. Experimental validation using university human data demonstrates that the model effectively quantifies movement accuracy and supports performance improvement through targeted training feedback. Results show a measurable increase in motion precision over three months, confirming the model's practical utility. The study concludes that fuzzy clustering, especially when fused with deep learning, provides a robust framework for human motion analysis in sports, offering coaches and athletes actionable insights for performance evaluation and tactical refinement. Read More Read Less