scholarly journals Image Recognition Method for Pitching Fingers of Basketball Players Based on Symmetry Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wanquan Chen
Keyword(s):  
Recognition Algorithm ◽  
Human Motion ◽  
Contour Detection ◽  
Finger Movement ◽  
Recognition Method ◽  
Basketball Players ◽  
Region Detection ◽  
Basketball Game ◽  
Human Motion Recognition ◽  
Segmentation Of Images

In the basketball game, the accuracy and standardization of pitching are directly related to the score. So it is very important to analyze the pitching figure movement to have a better positioning of the fingers. There are limited techniques to recognize the movement. The human motion recognition method is one of them. It utilizes the spatiotemporal image segmentation and interactive region detection to recognize images of pitching finger movement of basketball players. This method has a limitation that the symmetrical information of the human body and sphere cannot be excavated, which leads to certain errors in recognition effect. This paper presents a method of recognizing pitching finger movement of basketball players based on symmetry algorithm, constructs an acquisition model, carries out edge contour detection and adaptive feature segmentation of images of pitching finger movement of basketball players, and uses a fixed threshold to segment finger image to extract players’ hand contour and locate the middle axis of the finger. On this basis, the symmetry recognition method based on nematode recognition algorithm is used to recognize the symmetry of basketball pitching finger movement image and complete the accurate recognition of basketball pitching finger movement image. The experimental results show that the proposed method can effectively recognize the basketball player’s finger movement image. The average recognition accuracy is 98%, the growth rate of recognition speed is 98%, and the maximum recognition time is 12 s. The robustness of the proposed method is 0.45.

A Projection-based Human Motion Recognition Algorithm based on Depth Sensors

2021 ◽  
pp. 1-1
Author(s):  
Mu-Chun Su ◽  
Pang-Ti Tai ◽  
Jieh-Haur Chen ◽  
Yi-Zeng Hsieh ◽  
Shu-Fang Lee ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Depth Sensors ◽  
Human Motion Recognition

scholarly journals Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiulin Wang ◽  
Baole Tao ◽  
Fulei Han ◽  
Wenting Wei
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Accuracy ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Basketball Players ◽  
Human Actions ◽  
Basketball Game ◽  
Wide Range

The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.

scholarly journals Research On human motion recognition algorithm based on WLD

2021 ◽  
Vol 10 (8) ◽  
pp. 25390-25393
Author(s):  
SUN Qiu Feng ◽  
LI Xia
Keyword(s):  
Bayesian Method ◽  
Rapid Development ◽  
Wearable Devices ◽  
Recognition Algorithm ◽  
Human Motion ◽  
The State ◽  
Physiological Data ◽  
Comparative Effect ◽  
Human Motion Recognition ◽  
Intelligent Technology

With the rapid development of intelligent technology,People’s lives have gradually entered the era of information and intelligentce,Wearable devices are becoming more and more popular,it is easier to use sensors to obtain data,even physiological data,from human body.When large amounts of data are collected by sensors,we can analyze and model them.the values of each characteristic are used to judge the user’s state,then according to the state we can provide users with more accurate and convenient services. In this paper,the data collected by different sensors are used to establish a prediction model and analyze the comparative effect of different recognition algorithms on the test data. The results of the experiment shows that the Bayesian method based on WLD identities the state of the human body better.

A Multi-domain Fusion Human Motion Recognition Method Based on Lightweight Network

2021 ◽  
pp. 1-1
Author(s):  
Pengyun Chen ◽  
Qiang Jian ◽  
Peilun Wu ◽  
Shisheng Guo ◽  
Guolong Cui ◽  
...  
Keyword(s):  
Human Motion ◽  
Recognition Method ◽  
Motion Recognition ◽  
Domain Fusion ◽  
Human Motion Recognition

scholarly journals Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yang Ju
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
Recognition Rate ◽  
Linear Representation ◽  
Random Projection ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Human Motion Recognition

Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection. Firstly, the optical flow picture and Red, Green, Blue (RGB) picture obtained by the Lucas-Kanade algorithm are used. Secondly, the data of optical flow pictures and RGB pictures are compressed based on a random projection matrix of compressed sensing, which effectively reduces power consumption. At the same time, based on random projection compression data, it can effectively find the optimal linear representation to reconstruct training samples and test samples. Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer. Experimental results show that the algorithm in this paper significantly improves the recognition rate of multicategory actions and effectively reduces the computational complexity and running time of the recognition algorithm.

scholarly journals Human motion recognition based on SVM in VR art media interaction environment

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Fuquan Zhang ◽  
Tsu-Yang Wu ◽  
Jeng-Shyang Pan ◽  
Gangyi Ding ◽  
Zuoyong Li
Keyword(s):  
Genetic Algorithm ◽  
Dimensional Space ◽  
Dimensional Subspace ◽  
Recognition Algorithm ◽  
Human Motion ◽  
Support Vector ◽  
Motion Recognition ◽  
Linear Discriminant ◽  
Classification Feature ◽  
Human Motion Recognition

AbstractIn order to solve the problem of human motion recognition in multimedia interaction scenarios in virtual reality environment, a motion classification and recognition algorithm based on linear decision and support vector machine (SVM) is proposed. Firstly, the kernel function is introduced into the linear discriminant analysis for nonlinear projection to map the training samples into a high-dimensional subspace to obtain the best classification feature vector, which effectively solves the nonlinear problem and expands the sample difference. The genetic algorithm is used to realize the parameter search optimization of SVM, which makes full use of the advantages of genetic algorithm in multi-dimensional space optimization. The test results show that compared with other classification recognition algorithms, the proposed method has a good classification effect on multiple performance indicators of human motion recognition and has higher recognition accuracy and better robustness.

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