Human Motion Recognition Based on Multimodal Characteristics of Learning Quality in Football Scene

The task of human motion recognition based on video is widely concerned, and its research results have been widely used in intelligent human-computer interaction, virtual reality, intelligent monitoring, security, multimedia content analysis, etc. The purpose of this study is to explore the human action recognition in the football scene combined with learning quality related multimodal features. The method used in this study is to select BN-Inception as the underlying feature extraction network and use uncontrolled environment and real world to capture datasets UCFl01 and HMDB51, and pretraining is carried out on the ImageNet dataset. The spatial depth convolution network takes image frame as input, and the temporal depth convolution network takes stacked optical flow as input to carry out human action multimodal identification. In the results of multimodal feature fusion, the accuracy of UCFl01 dataset is generally high, all of which are over 80%, and the highest is 95.2%, while the accuracy of HMDB51 dataset is about 70%, and the lowest is only 56.3%. It can be concluded that the method of this study has higher accuracy and better effect in multimodal feature acquisition, and the accuracy of single-mode feature recognition is significantly lower than that of multimodal feature recognition. It provides an effective method for the multimodal feature of human motion recognition in the scene of football or sports.

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Research on Human Motion Recognition Based on Data Redundancy Technology

Complexity ◽

10.1155/2021/5542892 ◽

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.

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Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

Mathematical Problems in Engineering ◽

10.1155/2020/4358728 ◽

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.

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3D Dual Path Networks and Multi-scale Feature Fusion for Human Motion Recognition

2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ◽

10.1109/iaeac47372.2019.8997745 ◽

2019 ◽

Author(s):

Weisong Che ◽

Shuhua Peng

Keyword(s):

Feature Fusion ◽

Human Motion ◽

Motion Recognition ◽

Scale Feature ◽

Multi Scale ◽

Human Motion Recognition

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Radar Human Motion Recognition Using Motion States and Two-Way Classifications

2020 IEEE International Radar Conference (RADAR) ◽

10.1109/radar42522.2020.9114613 ◽

2020 ◽

Cited By ~ 1

Author(s):

Moeness G. Amin ◽

Ronny G. Guendel

Keyword(s):

Human Motion ◽

Motion Recognition ◽

Human Motion Recognition

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Improved One-Dimensional Convolutional Neural Networks for Human Motion Recognition

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm49941.2020.9313296 ◽

2020 ◽

Author(s):

Shengzhi Wang ◽

Shuo Xiao ◽

Zhenzhen Huang ◽

Zhiou Xu ◽

Wei Chen

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Human Motion ◽

Motion Recognition ◽

One Dimensional ◽

Human Motion Recognition

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Human Action Recognition Based on Multi-level Feature Fusion

2020 IEEE 6th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc51575.2020.9344943 ◽

2020 ◽

Author(s):

Xi Cai ◽

Wan Su ◽

Guang Han

Keyword(s):

Action Recognition ◽

Feature Fusion ◽

Human Action Recognition ◽

Human Action ◽

Multi Level

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Application of human motion recognition technology in extreme learning machine

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420983219 ◽

2021 ◽

Vol 18 (1) ◽

pp. 172988142098321

Author(s):

Anzhu Miao ◽

Feiping Liu

Keyword(s):

Extreme Learning Machine ◽

Research Work ◽

Human Motion ◽

Feature Descriptor ◽

Background Variable ◽

Motion Recognition ◽

Network Training ◽

Other Information ◽

Human Motion Recognition ◽

Learning Machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

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