A Review of Dynamic Maps for 3D Human Motion Recognition Using ConvNets and Its Improvement

Gao, Zhimin; Wang, Pichao; Wang, Huogen; Xu, Mingliang; Li, Wanqing

doi:10.1007/s11063-020-10320-w

Cited by 25 publications

(30 citation statements)

References 43 publications

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“…Assume that a decision i in a problem solution has a value of a i . Since the contribution of each decision to the performance of the problem solution c i is influenced by the values of other K decisions in addition to its own value [18,30] and the values of K decisions are represented by vector a k , the contribution of decision i to the performance of the problem solution is calculated as follows:…”

Section: Simulation Model Constructionmentioning

confidence: 99%

Application of Computer Simulation Analysis in Green City Garden Plant Landscape Design

Zhang¹

2021

Scientific Programming

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As an important component of urban landscape elements, garden plants have the role of improving urban ecological environment and forming landscape effects. Digital landscape is a high-simulation model of the system combined with computer hardware and software systems, which allow analysis of the application of computer simulation in the analysis of the value of urban landscape design. In this paper, we propose a “Task-Crowd-Process-Evaluation” computer simulation analysis framework to support management decision, which can form 2D or 3D spatial data, realize real-time and statistical analysis, make use of the multidimensional, efficient, and humanized human living environment, and make the environment landscape planning more reasonable and practical. The simulation shows that the effective use of urban vegetation can not only beautify the urban landscape, but also play the function of rainwater utilization.

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Section: Simulation Model Constructionmentioning

confidence: 99%

Application of Computer Simulation Analysis in Green City Garden Plant Landscape Design

Zhang¹

2021

Scientific Programming

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“…Earlier the depth information in the acquired scenes was captured by laser sensors. However, the depth values of many regions in the depth information captured by the laser sensor were unknown, so there were no corresponding depth values on many pixels in the synchronized color information [14,15]. In November 2011, a cheap, high-quality depth camera based on the principle of infrared scattering structured light appeared to capture the depth information in the scene.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Feature Recognition of Human Motion Behavior Based on Depth Sequence Analysis

Zhao

2021

Complexity

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The current research on still image recognition has been very successful, but the study of action recognition for video classes is still a challenging topic. In this work, we propose a random projection-based human action recognition algorithm to address the lack of depth information in color information (RGB video frames) that is not easily affected by environmental factors such as illumination and the lack of ability to recognize actions along the direction of view. A network structure is designed to take the obvious advantage of long- and short-term memory networks for controlling and remembering long sequences of historical information. The network structure in this paper is constituted by multiple memory units. At the same time, this paper constructs the spatial features, temporal features, and depth features of the three recognition stream outputs into a feature matrix, whose feature matrix is divided into multiple temporal segments according to the temporal dimension, then inputs them into the network layer in order, and achieves the fusion of the feature matrix in this paper according to their correlation characteristics on the temporal axis. Here, we proposed the concept of random batch projection operators. This basically uses as much sublimitation information as possible to improve projection accuracy by randomly selecting several subdependencies as projections defined during projection. A compressed sensing design of human motion acceleration data for low-power body area networks is proposed, and the basic idea and implementation process of compressed sensing theory for human motion data compression and reconstruction in wireless body area networks are introduced in detail.

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“…Compared with a traditional neural network, this algorithm adopts a convolution computation. The neurons between the convolutional layers of CNN are only connected with a few neurons between the adjacent layers, and the pooling and convolutional layers can respond to the translation invariance of the input features, effectively identifying the similar features of images (Bu, 2020;Gao et al, 2020). Moreover, CNN is composed of a convolutional layer for convolution operation, a pooling layer for feature screening, and a fully connected layer for feature fusion (Chen, 2019).…”

Section: Har Algorithm Based On Cnnmentioning

confidence: 99%

Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis

Liu

2021

Front. Psychol.

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In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm has been designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of a task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimised convolutional three-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80% with an image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimised C3D can recognise effectively human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports.

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A Review of Dynamic Maps for 3D Human Motion Recognition Using ConvNets and Its Improvement

Cited by 25 publications

References 43 publications

Application of Computer Simulation Analysis in Green City Garden Plant Landscape Design

Application of Computer Simulation Analysis in Green City Garden Plant Landscape Design

[Retracted] Feature Recognition of Human Motion Behavior Based on Depth Sequence Analysis

Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis

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