scholarly journals Human Action Recognition Technology in Dance Video Image

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lei Qiao ◽  
QiuHao Shen
Keyword(s):  
Probability Model ◽  
Recognition Rate ◽  
Human Action ◽  
Human Motion ◽  
Image Features ◽  
Video Image ◽  
Feature Model ◽  
Recognition Method ◽  
Recognition Time ◽  
Motion Recognition

In order to effectively improve the recognition rate of human action in dance video image, shorten the recognition time of human action, and ensure the recognition effect of dance motion, this study proposes a human motion recognition method of dance video image. This recognition method uses neural network theory to transform and process the human action posture in the dance video image, constructs the hybrid model of human motion feature pixels according to the feature points of human action in the image coordinate system, and extracts the human motion features in dance video image. This study uses the background probability model of human action image to sum the variance of human action feature function and update the human action feature function. It can also use Kalman filter to detect human action in dance video image. In the research process, it gets the human multiposture action image features according to the linear combination of human action features. Combined with the feature distribution matrix, it processes the human action features through pose transformation and obtains the human action feature model in the dance video image to accurately identify the human action in the dance video image. The experimental results show that the dance motion recognition effect of the proposed method is good, which can effectively improve the recognition rate of human action in dance video image and shorten the recognition time.

scholarly journals Research on Human Motion Recognition Based on Data Redundancy Technology

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Hong-Lan Yang ◽  
Meng-Zhe Huang ◽  
Zheng-Qun Cai
Keyword(s):  
Action Recognition ◽  
Feature Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Motion ◽  
Data Deduplication ◽  
Motion Recognition ◽  
Data Redundancy ◽  
Human Motion Recognition

Aiming at the problems of low recognition rate and slow recognition speed of traditional body action recognition methods, a human action recognition method based on data deduplication technology is proposed. Firstly, the data redundancy technology and perceptual hashing technology are combined to form an index, and the image is filtered from the structure, color, and texture features of human action image to achieve image redundancy processing. Then, the depth feature of processed image is extracted by depth motion map; finally, feature recognition is carried out by convolution neural network so as to achieve the purpose of human action recognition. The simulation results show that the proposed method can obtain the optimal recognition results and has strong robustness. At the same time, it also fully proves the importance of human motion recognition.

Machine-Printed Invoice Number Based on Fuzzy Recognition

2012 ◽  
Vol 214 ◽  
pp. 705-710 ◽  
Author(s):  
Xiao Ping Xian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real System ◽  
Recognition Method ◽  
Recognition Time ◽  
Recognition Ability ◽  
System Requirements ◽  
Fuzzy Recognition ◽  
Detection And Separation

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

scholarly journals Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Chao Tang ◽  
Huosheng Hu ◽  
Wenjian Wang ◽  
Wei Li ◽  
Hua Peng ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Motion ◽  
Image Features ◽  
Depth Image ◽  
Good Ability ◽  
Multimodal Features ◽  
Feature Based

The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.

Research on Action Recognition of Human Body Based on Kinect Sensor

2014 ◽  
Vol 644-650 ◽  
pp. 4162-4166
Author(s):  
Dan Dan Guo ◽  
Xi’an Zhu
Keyword(s):  
Action Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Motion ◽  
Skeletal Structure ◽  
Depth Sensor ◽  
Depth Sensors ◽  
3D Space ◽  
3D Information

An effective Human action recognition method based on the human skeletal information which is extracted by Kinect depth sensor is proposed in this paper. Skeleton’s 3D space coordinates and the angles between nodes of human related actions are collected as action characteristics through the research of human skeletal structure, node data and research on human actions. First, 3D information of human skeletons is acquired by Kinect depth sensors and the cosine of relevant nodes is calculated. Then human skeletal information within the time prior to current state is stored in real time. Finally, the relevant locations of the skeleton nodes and the variation of the cosine of skeletal joints within a certain time are analyzed to recognize the human motion. This algorithm has higher adaptability and practicability because of the complicated sample trainings and recognizing processes of traditional method is not taken up. The results of the experiment indicate that this method is with high recognition rate.

scholarly journals Research on Action Recognition Method of Dance Video Image Based on Human-Computer Interaction

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
FenTian Peng ◽  
Hongkai Zhang
Keyword(s):  
Human Computer Interaction ◽  
Action Recognition ◽  
Clustering Algorithm ◽  
Recognition Rate ◽  
Video Image ◽  
Recognition Method ◽  
Skeleton Model ◽  
Video Images ◽  
Motion Characteristics ◽  
Computer Interaction

Human-computer interaction technology simplifies the complicated procedures, which aims at solving the problems of inadequate description and low recognition rate of dance action, studying the action recognition method of dance video image based on human-computer interaction. This method constructs the recognition process based on human-computer interaction technology, constructs the human skeleton model according to the spatial position of skeleton, motion characteristics of skeleton, and change angles of skeleton, describes the dance posture features by generating skeleton node graph, and extracts the key frames of dance video image by using the clustering algorithm to recognize the dance action. The experimental results show that the recognition rate of this method under different entropy values is not less than 88%. Under the test conditions of complex, dark, bright, and multiuser interference, this method can make the model to describe the dance posture accurately. Furthermore, the average recognition rates are 93.43%, 91.27%, 97.15%, and 89.99%, respectively. It is suitable for action recognition of most dance video images.

scholarly journals Human motion recognition based on limit learning machine

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142093307
Author(s):  
Hong Chen ◽  
Hongdong Zhao ◽  
Baoqiang Qi ◽  
Shi Wang ◽  
Nan Shen ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Motion Capture ◽  
Recognition Rate ◽  
Human Motion ◽  
Motion Capture Data ◽  
Joint Position ◽  
Data Set ◽  
Motion Recognition ◽  
Human Motion Recognition ◽  
Learning Machine

With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action feature descriptors, this article makes improvements to features and classifiers and performs experiments on the Microsoft model specific register (MSR)-Action3D data set and the Bonn University high density metal (HDM05) motion capture data set. Based on displacement covariance descriptor and direction histogram descriptor, this article described both combine to produce a new combination; the description can statically reflect the joint position relevant information and at the same time, the change information dynamically reflects the joint position, uses the extreme learning machine for classification, and gets better recognition result. The experimental results show that the combined descriptor and extreme learning machine recognition rate on these two data sets is significantly improved by about 3% compared with the existing methods.

scholarly journals Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zaosheng Ma
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Action Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Cultural Tourism ◽  
Human Action ◽  
Human Motion ◽  
Tourism Industry ◽  
Som Neural Network

Smart cultural tourism is the development trend of the future tourism industry. Virtual reality is an important tool to realize smart tourism. The reality of virtual reality mainly comes from human-computer interaction, which is closely related to human action recognition technology. Therefore, the research takes human action recognition as the research direction, uses a self-organizing mapping network (SOM) neural network to extract the key frame of action video, combines it with multi-feature vector method to recognize human action, and compares the recognition rate and user satisfaction of different recognition methods. The results show that the recognition rate of multi-feature voting human action recognition algorithm based on SOM neural network is 93.68% on UT-Kinect action, 59.06% on MSRDailyActivity3D, and the overall action recognition time is only 3.59 s. Within six months, the total profit of human-computer interactive virtual reality tourism project with SOM neural network multi-eigenvector as the core algorithm reached 422,000 yuan, and 88% of users expressed satisfaction after use. It shows that the proposed method has a good recognition rate and can give users effective feedback in time. It is hoped that this research has a certain reference value in promoting the development of human motion recognition technology.

scholarly journals Research on an Improved MB-LBP 3D Face Recognition Method

2021 ◽  
pp. 306-314
Author(s):  
Liangliang Shi ◽  
◽  
Xia Wang ◽  
Yongliang Shen
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Depth Image ◽  
Support Vector ◽  
3D Face Recognition ◽  
Recognition Method ◽  
Recognition Time ◽  
3D Face ◽  
Feature Information ◽  
Average Information

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.

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

Paper Currency Image Features Extraction and Recognition Based on Gabor Filter

2014 ◽  
Vol 635-637 ◽  
pp. 1030-1034 ◽  
Author(s):  
Xi Wen Liu ◽  
Chao Ying Liu
Keyword(s):  
Gabor Filter ◽  
Recognition Rate ◽  
Image Features ◽  
Recognition Method ◽  
Match Method ◽  
Template Match ◽  
Multi Scale ◽  
Paper Currency ◽  
Texture Characteristics ◽  
Directional Texture

The paper currency image recognition method based on Gabor filter set is discussed in this paper. According to the paper currency image features, the suitable parameters of Gabor filter set are selected for the extraction of paper currency characteristics, the multi-scale and multi-directional texture characteristics of paper currency image are gotten; then the texture images are meshed, and the row and column projection sum of grid pixels' average grey are calculated, finally, the template match method based on grid projection characteristics is used for paper currency recognition. Experiments show that, this method has strong anti-interference ability, it can raise the recognition rate of old or dirty paper currency greatly, and it costs little time.

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