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.

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 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.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

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.

scholarly journals I3D-Shufflenet Based Human Action Recognition

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 301
Author(s):  
Guocheng Liu ◽  
Caixia Zhang ◽  
Qingyang Xu ◽  
Ruoshi Cheng ◽  
Yong Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Convolution Kernel ◽  
Histogram Of Oriented Gradients ◽  
Temporal Features ◽  
Convolution Kernels

In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.

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