scholarly journals Intelligent Recognition Method of Athlete Wrong Movement Based on Image Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wang Lu ◽  
JiangYuan Hou
Keyword(s):  
Human Body ◽  
Body Movement ◽  
Three Dimensional ◽  
Image Data ◽  
Recognition Method ◽  
Canny Operator ◽  
Cloud Data ◽  
Movement Recognition ◽  
Cmos Sensor ◽  
Intelligent Recognition

Current methods of human body movement recognition neglect the depth denoising and edge restoration of movement image, which leads to great error in athletes’ wrong movement recognition and poor application intelligence. Therefore, an intelligent recognition method based on image vision for sports athletes’ wrong actions is proposed. The basic principle, structure, and 3D application of computer image vision technology are defined. Capturing the human body image and point cloud data, the three-dimensional dynamic model of sports athletes action is constructed. The color camera including CCD sensor and CMOS sensor is selected to collect the wrong movement image of athlete and provide image data for the recognition of wrong movement. Wavelet transform coefficient and quantization matrix threshold are introduced to denoise the wrong motion images of athletes. Based on this, the feature of sports athlete’s motion contour image is extracted in spatial frequency domain, and the edge of the image is further recovered by Canny operator. Experimental results show that the proposed method can accurately identify the wrong movements of athletes, and there is no redundancy in the recognition results. Image denoising effect is good and less time-consuming and can provide a reliable basis for related fields.

Kinect Depth Data Segmentation Based on Gauss Mixture Model Clustering

2013 ◽  
Vol 760-762 ◽  
pp. 1556-1561
Author(s):  
Ting Wei Du ◽  
Bo Liu
Keyword(s):  
Mixture Model ◽  
Point Cloud ◽  
Three Dimensional ◽  
Image Data ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Depth Image ◽  
Cloud Data ◽  
Mixture Model Clustering ◽  
Gauss Mixture

Indoor scene understanding based on the depth image data is a cutting-edge issue in the field of three-dimensional computer vision. Taking the layout characteristics of the indoor scenes and more plane features in these scenes into account, this paper presents a depth image segmentation method based on Gauss Mixture Model clustering. First, transform the Kinect depth image data into point cloud which is in the form of discrete three-dimensional point data, and denoise and down-sample the point cloud data; second, calculate the point normal of all points in the entire point cloud, then cluster the entire normal using Gaussian Mixture Model, and finally implement the entire point clouds segmentation by RANSAC algorithm. Experimental results show that the divided regions have obvious boundaries and segmentation quality is above normal, and lay a good foundation for object recognition.

scholarly journals An EFE Model on Skin-Sleeve Interactions During Arm Rotation

2006 ◽  
Vol 128 (6) ◽  
pp. 872-878 ◽  
Author(s):  
Malcolm M. Q. Xing ◽  
Zhiguo Sun ◽  
Ning Pan ◽  
Wen Zhong ◽  
Howard I. Maibach
Keyword(s):  
Human Body ◽  
Body Movement ◽  
Three Dimensional ◽  
Dynamic Contact ◽  
Shear Stresses ◽  
Explicit Finite Element ◽  
Garment Design ◽  
First Time ◽  
General Guidance ◽  
Injury Potential

Skin and garment constitute a dynamic contact system for human body comfort and protection. Although dermatological injuries due to fabric actions during human body movement are common, there is still no general guidance or standard for measuring or evaluating skin/garment contact interactions, especially, during intense sports. A three-dimensional explicit finite element (EFE) model combined with Augmented Lagrange algorithm (ALA) is developed to simulate interactions between skin and fabric during rotation of the arm. Normalized effective shear stresses at the interface between skin and the sleeve during the arm rotation are provided to reflect the severity of the interactions. The effects due to changes in fabric properties, fabric-skin gap, and arm rotation rate are also illustrated. It has been demonstrated from our predictions that factors such as elastic modulus, friction coefficients, density of fabric, and the initial gap between skin and fabric influence significantly the shear stress and thus the discomfort and even injury potential to skin during intensive body movement such as sports and military. Thus this study for the first time confirms quantitatively that poorly chosen fabric with inappropriate garment design renders adverse actions on human skin.

scholarly journals Aerobics Action Recognition Algorithm Based on Three-Dimensional Convolutional Neural Network and Multilabel Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qian Wang ◽  
Mingzhe Wang
Keyword(s):  
Three Dimensional ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Multilabel Classification ◽  
Convolutional Networks ◽  
Human Movements ◽  
Video Recognition ◽  
Intelligent Recognition ◽  
Time Information

In the context of modern people increasingly paying attention to health and promoting aerobics, the amount of data and audiences of aerobics videos has grown rapidly, and its potential application value has attracted widespread attention from scientific research and industry perspectives. This article has integrated computer vision and deep learning related knowledge to realize the intelligent recognition and representation of specific human movements in aerobics video sequences. The study proposes an automatic recognition method for floor exercise videos based on three-dimensional convolutional networks and multilabel classification. Since two-dimensional convolutional neural networks (CNNs) lose time information when extracting features, so to overcome this, the proposed research uses three-dimensional convolutional networks to perform video recognition. The feature is taken in time and space, and the extracted features are subjected to multiple binary classifications to achieve the goal of multilabel classification. Various comparison and simulation experiments are conducted for the proposed research, and the experimental results prove the effectiveness and superiority of the approach.

Technology of Data Conversion and Reconstruction for Complex Surface Based on Non-Contact Measurement

2014 ◽  
Vol 685 ◽  
pp. 614-617 ◽  
Author(s):  
Jie Cai
Keyword(s):  
Physical Model ◽  
Human Body ◽  
Point Cloud ◽  
Three Dimensional ◽  
Data Conversion ◽  
Point Cloud Data ◽  
Human Body Model ◽  
Cloud Data ◽  
Contact Measurement ◽  
Body Model

Based on the technology of non-contact measurement, this paper has researched on complex curved surface physical modeling and data conversion technology, and has been applied to the human body modeling and data conversion in costume design. The measurement principle of grating projection is used to collect the point cloud data of surface of the physical model in a three-dimensional space. The point cloud data should be preprocessed with noise rejection, multi-view stitching and data reduction by Geomagic Studio software. Then the relatively regular surface area can be gotten by using parameter transformation, through two different ways. After that, the model surface data should be converted into Solidworks parts. By comparison and optimization, a better three-dimensional surface is gotten. A standard database of human body model has been set up and the main parameters of human body model data will be obtained by combined with non-contact three-dimensional measurement system. After all, part of the parameterization of the physical model has been realized through the work of invoking the model of the standard library, comparing the standard model with measured body model, doing the error analysis and so on.

scholarly journals Accurate Recognition Method of Human Body Movement Blurred Image Gait Features Using Graph Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Yu
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Feature Recognition ◽  
Body Movement ◽  
Human Movement ◽  
Recognition Method ◽  
Gait Features ◽  
Gait Feature ◽  
Blurred Image ◽  
Blurred Images

In view of the problems of low precision, poor quality, and long time of gait feature recognition due to the influence of human body movement environment on the recognition process of the current gait feature recognition method of human body movement blurred image, a new method of gait feature recognition based on graph neural network (GNN) method is proposed. The gait features of human movement blurred images were extracted, and the fusion clustering recognition of the GNN algorithm was used to locate the gait features of human movement blurred images. The gait features of human body movement blurred images were located by the GNN method. According to the contour feature point info of the human body movement blurred image, the standard deviation of gait feature location of the human body movement blurred image was calculated, the gait feature of the blurred image of human body movement was reconstructed, and the gait recognition of the human body movement blurred image was achieved. The results show that the extraction of human movement is good, with high positioning confidence, good recognition quality, average recognition accuracy of 92%, and greatly shortened recognition time.

scholarly journals Posture Estimation of a Human Body from Thermal Images of 2D Appearance Models of 3D Ellipsoidal Model

2009 ◽  
Vol 13 (3) ◽  
pp. 172-177 ◽  
Author(s):  
Mitsuhiro Hayase ◽  
◽  
Susumu Shimada ◽  
Keyword(s):  
Human Body ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Recognition Method ◽  
Thermal Images ◽  
New Model ◽  
Model Based ◽  
Ellipsoidal Model ◽  
Posture Estimation ◽  
Appearance Models

We propose a new model-based recognition method that involves the use of three-dimensional (3D) ellipsoidal models in various sizes and proportions as well as their two-dimensional (2D) appearance models. Most model-based vision is intended to recognize specified objects, and the model is specific to the object. However, our method can recognize various proportions of objects and was applied in posture estimation of the human body from thermal images.

scholarly journals AUTOMATIC BUILDING EXTRACTION FROM LIDAR POINT CLOUD DATA IN THE FUSION OF ORTHOIMAGE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 541-546
Author(s):  
B. Hujebri ◽  
M. Ebrahimikia ◽  
H. Enayati
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Mean Shift ◽  
Three Dimensional ◽  
Image Data ◽  
Aerial Image ◽  
Normal Vector ◽  
Cloud Data ◽  
Mean Shift Algorithm ◽  
Research Experiment

Abstract. Three-dimensional building models are important in various applications such as disaster management and urban planning. In this paper, a method based on the fusion of LiDAR point cloud and aerial image data sources has been proposed. The first step of the proposed method is to separate ground and non-ground (that contain 3d objects like buildings, trees, …) points using cloth simulation filtering and then normalize the non-ground points. This research experiment applied a 0.1 threshold for the z component of the normal vector to remove wall points, and 2-meter height threshold to remove off-terrain objects lower than the minimum building height. It is possible to discriminate vegetation and building based on spectral information from orthoimage. After elimination of vegetation points, the mean shift algorithm applied on remaining points to detect buildings. This method provides good performance in dense urban areas with complex ground covering such as trees, shrubs, short walls, and vehicles.

scholarly journals A Novel Method for Density Analysis of Repaired Point Cloud with Holes Based on Image Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3417
Author(s):  
Yibo He ◽  
Zhenqi Hu ◽  
Kan Wu ◽  
Rui Wang
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Image Data ◽  
Image Point ◽  
Point Cloud Data ◽  
Three Dimensional Model ◽  
Cloud Data ◽  
Density Analysis ◽  
Novel Method ◽  
Density Image

Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.

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