[Retracted] Human Posture Recognition and Estimation Method Based on 3D Multiview Basketball Sports Dataset

Abstract

In traditional 3D reconstruction methods, using a single view to predict the 3D structure of an object is a very difficult task. This research mainly discusses human pose recognition and estimation based on 3D multiview basketball sports dataset. The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. This research uses some modules of the VGG11 network. For different feature fusion methods, different modules of the VGG11 network are used as the feature extraction network. In order to be efficient in computing and processing, the multilayer perceptron in the network model is implemented by a one-dimensional convolutional network. The input is a randomly sampled point set, and after a layer of perceptron, it outputs a feature set of n × 16. Then, the feature set is sent to two network branches, one is to continue to use the perceptron method to generate the feature set of n × 1024, and the other network is used to extract the local features of points. After the RGB basketball sports picture passes through the semantic segmentation network, a picture containing the target object is obtained, and the picture is input to the constructed feature fusion network model. After feature extraction is performed on the RGB image and the depth image, respectively, the RGB feature, the local feature of the point cloud, and the global feature are spliced and fused to form a feature vector of N × 1152. There are three branches for this vector network, which, respectively, predict the object position, rotation, and confidence. Among them, the feature dimensionality reduction is realized by one-dimensional convolution, and the activation function is the ReLU function. After removing the feature mapping module, the accuracy of VC-CNN_v1 dropped by 0.33% and the accuracy of VC-CNN_v2 dropped by 0.55%. It can be seen from the research results that the addition of the feature mapping module improves the recognition effect of the network to a certain extent

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

“…Regarding the used data for the development and testing of the systems, on the one hand, several papers such as [ 12 , 13 , 14 , 15 , 17 , 18 , 20 , 37 , 38 , 42 ], developed their own datasets using manual annotations, MoCap systems, or other ground truth generation methods, but did not make them publicly available. Other papers created and published their dataset to contribute to the research community, such as [ 22 , 30 ].…”

“…Several approaches are trying to solve specific estimation problems in different environments, such as the ones for basketball [ 14 ], diving [ 24 ], hockey [ 35 ], etc, while others try to create a general sports use system, such as [ 39 , 41 ]. Taking into account the limited amount of work in specific sports, we can say that interesting research and development can be found regarding HPE and hockey .…”

“…Apart from the previously presented metrics, the use of other metrics can be seen in the works by several authors, such as Root-Mean-Square Error (RMSE) and Mean Per Joint Position Error (MPJPE) [ 12 ], or their own evaluation metrics, including some parameters from the estimation that are not normally included, such as Frames Per Second (FPS) in combination with sensitivity and precision in [ 13 ] or their own accuracy calculation methods that are explained in the papers [ 14 , 15 ]. Another metric used in some evaluations is Percentage of Correct Parts (PCP), which calculates the detection rate of limbs.…”

“…(in such actions in which skeleton is pushing, current HPE methods show quite high error for the objective of the paper, at least for the proposed method) RGB images + depth Output: 3D Own not public dataset created using maker-based MoCap system Qualysis and markerless OpenPose system to record sprints. Human Posture Recognition and Estimation Method Based on 3D Multiview Basketball Sports Dataset [ 14 ] 3D HPE using multiview basketball sports dataset. RGB images + depth Output: 3D ModelNet40 (CAD models with category label) Own basketball dataset which is not publicly available ( the annotation process is not indicated ) A Mobile Application for Running Form Analysis Based On Pose Estimation Technique [ 15 ] 2D HPE applied for running form analysis using a phone.…”

Study on the Segmentation Method of the Improved DeepLabv3+ Algorithm in the Basketball Scene

“…After four downsamplings, the resolution is only 1/16 of the original, that is, 32 * 32. Although advanced features are extracted from the network, a large amount of low-level feature information is discarded, which makes the image blurred and difficult to recover in upsampling [14].…”