Model transfer from 2D to 3D study for boxing pose estimation

IntroductionBoxing as a sport is growing on Chinese campuses, resulting in a coaching shortage. The human pose estimation technology can be employed to estimate boxing poses and teach interns to relieve the shortage. Currently, 3D cameras can provide more depth information than 2D cameras. It can potentially improve the estimation. However, the input channels are inconsistent between 2D and 3D images, and there is a lack of detailed analysis about the key point location, which indicates the network design for improving the human pose estimation technology.MethodTherefore, a model transfer with channel patching was implemented to solve the problems of channel inconsistency. The differences between the key points were analyzed. Three popular and highly structured 2D models of OpenPose (OP), stacked Hourglass (HG), and High Resolution (HR) networks were employed. Ways of reusing RGB channels were investigated to fill up the depth channel. Then, their performances were investigated to find out the limitations of each network structure.Results and discussionThe results show that model transfer learning by the mean way of RGB channels patching the lacking channel can improve the average accuracies of pose key points from 1 to 20% than without transfer. 3D accuracies are 0.3 to 0.5% higher than 2D baselines. The stacked structure of the network shows better on hip and knee points than the parallel structure, although the parallel design shows much better on the residue points. As a result, the model transfer can practically fulfill boxing pose estimation from 2D to 3D

Towards using artificial intelligence as tool in artistic gymnastics coaching : case backward giant circle

The objective of this thesis was to study whether it is possible to create a system that estimates artistic gymnast’s body joint angles based on a low-budget 2-dimensional single RGB video recording. To meet the objective, 54 video files were collected on gymnasts performing backward giant circle skill, together with assessments of the performances by two professional coaches. The video files contained total of 233 repetitions of the skill. A pilot system of computer vision algorithms was developed, using an open source human body pose recognition algorithm. An algorithm based on pixel grayscale value was developed and used to recognize starting and ending moment of a repetition and to sample each repetition at 7 key phases. Body joint angle estimates were calculated based on the body part location estimates of the 1631 samples. The work proved that it is possible to develop a system that estimates body joint angles of an artistic gymnast. It was found that rotation and cropping of the frames improved probability of yielding correct estimates. The angle estimate for knees had highest, up to 66%, correlation with coach evaluations. Hips and shoulders had weak but significant correlation with coach evaluations. The results indicate that it is possible to develop a low-budget system that could work as augmented tool in artistic gymnastics coaching. In addition, human body pose recognition provides a new method to biomechanical research of artistic gymnastics