Automatic motion recognition technology based on fuzzy clustering algorithm and VR video image

The rapid development of Virtual Reality (VR) technology has heightened interest in immersive video applications. Current dynamic video segmentation methods emphasize iterative pixel-level classification based on fuzzy membership levels. Traditional motion recognition, however, struggles with posture variations in complex scenarios, leading to inaccuracies. To address this, we propose a fuzzy clustering-based framework for VR video motion recognition, enhancing accuracy through optimized pixel segmentation and preprocessing. The system’s stability across varying frame rates was evaluated. Experiments utilized a CCD-captured database of six motion poses (sitting, squatting, kneeling, etc.). Results demonstrated 96% accuracy for squat detection, 6.7% false positives for kneeling postures, and a 2%–8% error range for sitting poses—significantly higher than other categories due to postural variability. This study advances VR motion recognition by integrating fuzzy clustering with adaptive image processing, offering robust solutions for immersive training, rehabilitation monitoring, and human–computer interaction systems.