Research On Location And Trajectory Prediction Of Table Tennis In Complex Background

With the rapid development of computer application,machine vision,deep learning and other technologies,dynamic target space motion trajectory has high research significance and application value in military,transportation,sports,industry and other fields.In a complex background,the feature of the target object is difficult to be separated from the background,which is easy to lead to the target identification error,and the real-time performance and accuracy of the algorithm are difficult to be balanced.In addition,the existing algorithm has problems such as frame missing and large measurement error,which is easy to lead to the feature missing of the input data of trajectory prediction.In this paper,taking sports table tennis as the research object,dynamic target positioning and motion trajectory prediction were carried out,a system test platform was built,algorithm research and experimental verification were carried out,and the research contents were as follows:(1)In view of the difficulties of traditional algorithms in dealing with the complex background,light changes,small target and fast speed of sports table tennis,a sports table tennis detection system was built by using three black and white high-speed industrial cameras,and the recognition and positioning algorithm of sports table tennis was studied.For the three-dimensional reconstruction of table tennis,the improved YOLOv4-Tiny object recognition model combined with image processing technology is used to extract the center point of table tennis for pixel matching.The research content includes multi-target targeting,image acquisition,data set making,model training,image processing,three-dimensional reconstruction and other research contents.In order to improve the accuracy of trajectory extraction system,a method of dynamic weight multi-camera information fusion was proposed.Experiments verified the requirements of recall rate,recognition accuracy,real-time performance and positioning accuracy of the recognition model of sports ping-pong balls.(2)Aiming at the research of trajectory prediction of sports table tennis,a trajectory prediction model combining traditional physical model and improved GRU neural network was proposed to solve the problem of missing long-term memory information in traditional physical model.Through the force analysis of table tennis,a physical model of moving table tennis is built.The data input neural network is constrained,and the trajectory data is introduced into the physical model for data expansion.In order to solve the problem that the traditional network cannot rely on and gradient explosion for a long time,an improved GRU neural network was used to predict the trajectory of a moving table tennis ball.A dynamic weight fusion method was proposed to fuse the data information of the front and back frames,and the context information was fused.The prediction accuracy and stability of the improved trajectory prediction model SPM-GRU were verified by experiments.(3)Based on.NET platform,dynamic target identification,positioning and trajectory prediction system is developed,including system structure,function module,database structure and function module design,and the overall function of the system is tested and verified.Experimental tests show that the system has perfect functions and meets the expected requirements in real-time,stability and accuracy.It can be used as a reference for other similar applications and has good engineering value.