Volleyball is a team sport of track video-based analysis essential. However, this is difficult, especially in machine vision and wearable devices. Due to the ball’s small size, its speed of motion blur is generated, generally blocked. Older systems find it difficult to analyze the level of volleyball. When tracking with machine vision and wearable devices, it is recommended that Volleyball Motion Estimation Algorithms improve robustness to player confusion between different particle series. Faster processing speed and less confusing players presented herein as a method superior to the conventional particulate filter. The ball and Volleyball Motion Estimation Algorithm uses ma- chine vision and wearable devices to detect differential picture, and motion machine vision wearable device is optimized. Optimized toss Volleyball is correct by the Volleyball Motion Estimation Algorithm, and the trajectorys almost equal to the false prediction of the ball’s size. Machine vision and wearable devices can provide a new sports attendance watching experience through predictive images superimposed on live broadcasts. This also shows that the method can identify some important parts of the body to help predict thrown. Compared to the previous system, which provides better results.
Keywords: Volleyball motion estimation algorithm | Machine vision and wearable devices | Optimization of ball tracking | Classical particle filter | Predicted images