RETRACTED: Dynamic image sampling and swimming motion image recognition in immersive virtual reality

Abstract

Video Analysis Underwater Virtual Reality Correction, common in technical analysis, supports elite swimming coaches and athletes as a visual aid. The underwater visual environment and dynamic camera behavior can degrade the video, which is considered an undesirable challenge. The athlete's movement can also blur the field of vision and create a cavitation effect on the air bubbles. Detects problems with existing systems configured using multiple image processing algorithms. The Naive Bayes algorithm studied swimming motion analysis and applied a video of broadcast sports to recognize immersive virtual reality by swimming and proposed here. The process is more sensitive to noise than global motion recovery, 1) local motion is usually buried in a complex motion, 2) clutter containing multiple objects: local motion analysis is for two reasons. It's difficult. However, a useful method for local motion analysis is to understand significant human activity from image sequences. In this study, Naive Bayes uses third robust motion estimation and prominent colors to sample detected objects and extract swimming motion. Swimmer From the moving image based on the above view, the observed wave resistance is observed through the suspension camera. The recorder data is synchronized with the immersive virtual reality of evaluating the actual moving image to capture detailed swimming movements. Principal Component Analysis (PCA) is analyzed by time-frequency. These recorders' data are displayed in chronological order with a different swimming immersion virtual reality than the main frequency.