Recently, sports technology gradually develops and changes the training process in swimming. With the assistance of professional photographic equipment and wearable devices, the design of the swimming pool is gradually moving towards intelligence. The integrated system of wearable devices and photographic equipment is available and used to make quantitative statics during training process like in-water pose, velocity and angle of swimmer. Although this kind of system is useful for swimmer’s performance analysis, the cost of time and money is expensive. In this study, we provide two analysis systems for diving and swimming respectively, which eliminates the cumbersome setup and cost mentioned above. We can only use 2D RGB video recorded by single camera to estimate the positions of skeleton joints. These two systems include the data required by the coach, such as hip angle, velocity, diving distance, airtime, frequency, and distance of swimming stroke. Also, both systems provide the trajectory of athlete. Both analysis systems use a modified version of Openpose. By adding Densenet architecture and decreasing the number of PAF stages, the size of model can be reduced. We can save the model training and testing time. The precision of skeleton joint position estimation is maintained. The training data is based on swimming coach and Youtube. Then, according to the estimation of skeleton given by the model, we provide correction algorithm to deal with several error cases(e.g. partial joint missing problem) to obtain more accurate skeleton joint location information for the subsequent quantitative data computation.
Skeleton Joint Estimation, Correction and Analysis Using Modified Openpose Network for Diving and Swimming
崇名, 魏. (Author). 2021
Student thesis: Master's Thesis