Design And Implementation Of Motion Video Analysis System Based On Deep Learning Model

When doing technical action analysis in the field of sports events,the way of using professional sports photography equipment to record to get the movement process and then combining with technical experts to analyze is difficult to meet the needs of the increasingly large user groups and sports guidance market in terms of cost and efficiency.As the research results of deep learning in the field of computer vision are gradually applied to sports,transportation,medical and other industries,deep learning-based motion video analysis has also become a popular direction.A pain point of current sports technology analysis is the need to rely on professional large-scale scene equipment and later manual annotation analysis.The thesis is to improve the efficiency as well as versatility of motion video analysis as a starting point,design and implement a motion video analysis system serving mobile devices.The input data of the system is the motion process data captured from the real world,and after a series of deep learning algorithm processing to extract the original motion data,using these data to analyze and process the final motion feature information for training feedback and technology enhancement.The thesis first investigates and introduces the technical background related to the motion video analysis system,including image calibration technology,semantic segmentation technology,and server-side development technology,etc.,and then describes the requirement analysis and outline design of the motion video analysis system,which is divided into three major modules:system general module,motion analysis module,and service processing module,and then focuses on the image calibration technology and a general The analysis,design and implementation of the motion feature information analysis algorithm are then highlighted.The detailed design of the system and functional modules around the algorithm is also presented,and the definition and methods of each class in the functional modules are further described,as well as how to use these classes to implement the complete system.Finally,the entire system is tested with comprehensive test cases for each function and performace testing.