Research on Protection Property of Running Sportswear Fabrics Based on 3-D Motion Capture System

Wang, Shikui; Xie, Hong Tao; Hu, Bing

doi:10.14355/tlist.2014.03.009

Cited by 19 publications

(3 citation statements)

References 1 publication

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“…e template-based matching method is to define a hierarchical human body template for the pedestrian target to be detected, then compare and match the image to be detected with each area of the template, and finally determine whether the target to be detected is a pedestrian based on its matching similarity [12]. e method based on statistical learning refers to obtaining a classifier representing pedestrians by training the image sample data and then using the classifier to judge and classify the image to be detected [13]. Statistics-based methods can be divided into support vector machine (SVM), AdaBoost, and neural network-based methods according to their classifiers.…”

Section: Related Workmentioning

confidence: 99%

Research on Fast Pedestrian Detection Algorithm Based on Autoencoding Neural Network and AdaBoost

Zhou

2021

Complexity

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In order to solve the problem of low accuracy of pedestrian detection of real traffic cameras and high missed detection rate of small target pedestrians, this paper combines autoencoding neural network and AdaBoost to construct a fast pedestrian detection algorithm. Aiming at the problem that a single high-level output feature map has insufficient ability to express pedestrian features and existing methods cannot effectively select appropriate multilevel features, this paper improves the traditional AdaBoost algorithm structure, that is, the sample weight update formula and the strong classifier output formula are reset, and the two-input AdaBoost-DBN classification algorithm is proposed. Moreover, in view of the problem that the fusion video is not smoothly played, this paper considers the motion information of the video object, performs pixel interpolation by motion compensation, and restores the frame rate of the original video by reconstructing the dropped interframe image. Through experimental research, we can see that the algorithm constructed in this paper has a certain effect.

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Section: Related Workmentioning

confidence: 99%

Research on Fast Pedestrian Detection Algorithm Based on Autoencoding Neural Network and AdaBoost

Zhou

2021

Complexity

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“…This article uses English speech sounds to identify students, and introduces the extracted features and classification methods. We assume that the sample data of English speech sounds is [16]:…”

Section: Analysis Of Immersive Algorithms Based On Digital Twin Techn...mentioning

confidence: 99%

English Classroom Immersive Teaching Based on Digital Twin Technology

Huang

2021

Preprint

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In order to improve the effect of English classroom teaching, this paper builds an immersive teaching model of English classroom based on digital twin technology. Moreover, in order to reduce the cost of the system, simplify the system structure, and enable learners to observe the stereoscopic display image with the naked eye, this paper builds an immersive hemispherical projection teaching system, designs the overall structure of the system and various components of the hardware, and studies the projection image predistortion algorithm. In addition, this paper uses the immersive experience between virtual reality technology and immersive teaching as a connection point to design an immersive teaching based on VR and apply it to teaching practice to test the effect of immersive teaching, and perfect the immersive teaching design model based on VR. Finally, this paper designs an experiment to verify and analyze the performance of the system. From the experimental results, it can be seen that the English classroom immersive teaching model based on digital twin technology constructed in this paper is effective.

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“…Zhai et al [ 17 ] used the SVM algorithm, and Setz et al [ 18 ] carried out the classification using DFA and SVM algorithms. At present, there are relatively few studies at home and abroad on affective computing in adverse states, especially for different stress levels.…”

Section: Introductionmentioning

confidence: 99%

The Method for Identifying Employees’ Emotions in Adverse States Incorporating PSO-kNN Algorithm and Multiple Physiological Parameters

Han¹

2022

Computational Intelligence and Neuroscience

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It is well known that we, as human beings, are prone to a variety of undesirable emotions such as excitement, boredom, and fear, all of which are induced by varying degrees of negative states. In this paper, we designed an emotion-evoking experiment to induce calm, excited, bored, and fearful emotions, as well as low, moderate, and high levels of tension. Based on the six physiological signals such as heart rate and respiration rate of the subjects in these emotion states, feature extraction was performed after removing the baseline preprocessing, combined with particle swarm optimisation algorithm for feature selection, and the k-nearest neighbour algorithm was used to classify the different emotion and tension levels in the undesirable states. By comparing the results of several sets of experiments, we found that with baseline removal and particle swarm feature selection optimisation, our experimental results using k-nearest neighbour classification showed a significant improvement in recognition accuracy compared to the traditional k-nearest neighbour algorithm, which indicates that the proposed method has better recognition results.

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Research on Protection Property of Running Sportswear Fabrics Based on 3-D Motion Capture System

Cited by 19 publications

References 1 publication

Research on Fast Pedestrian Detection Algorithm Based on Autoencoding Neural Network and AdaBoost

Research on Fast Pedestrian Detection Algorithm Based on Autoencoding Neural Network and AdaBoost

English Classroom Immersive Teaching Based on Digital Twin Technology

The Method for Identifying Employees’ Emotions in Adverse States Incorporating PSO-kNN Algorithm and Multiple Physiological Parameters

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