Bilayer model for cross-view human action recognition based on transfer learning

Li, Yandi; Xu, Xiping; Xu, Jiahong; Du, Enyu

doi:10.1117/1.jei.28.3.033016

Cited by 5 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We employ interquartile distance to locate outliers in this article. Although the interquartile distance represents the degree of dispersion of each variable in statistical data, it is more trustworthy statistical data [16]. As indicated in the table, all values are sorted in the sample from small to big and then split the data into quartiles with three points, which are quartiles (Table 2).…”

Section: Numerical Calculation Of Force Changes Of Limb Movements In ...mentioning

confidence: 99%

[Retracted] Detection Method of Limb Movement in Competitive Sports Training Based on Deep Learning

Qin¹,

Huo

2022

Journal of Mathematics

View full text Add to dashboard Cite

Traditional methods have the problems of insufficient accuracy and slow speed in human posture detection. In order to solve the above problems, a limb movement detection method in competitive sports training based on deep learning is proposed. The force change parameters of sports limb movements in the process of sports are computed to achieve the detection of limb movements in competitive sports training, and the limb movement characteristics in competitive sports training are extracted using a deep learning algorithm. The experimental results show that the limb movement detection method based on deep learning in competitive sports training has significantly higher detection accuracy and faster speed.

show abstract

Section: Numerical Calculation Of Force Changes Of Limb Movements In ...mentioning

confidence: 99%

[Retracted] Detection Method of Limb Movement in Competitive Sports Training Based on Deep Learning

Qin¹,

Huo

2022

Journal of Mathematics

View full text Add to dashboard Cite

show abstract

“…From Figure 3, image information is input into GCN, and node information is integrated through edge and corner information. Node information is created based on nodes, and then output to classify different information [13]. Single layer GCN is calculated by equation ( 4).…”

Section: Traditional Convolution Involves Selecting Pixels Aroundmentioning

confidence: 99%

“…In equation ( 12), t represents the size of the time kernel, which can control the time constraints included in the neighbor map [18]. The transformation of spatio-temporal maps also requires the participation of model functions and weight functions, as shown in equation (13).…”

Section: D Recognition Algorithm Based On St-gcnmentioning

confidence: 99%

Application of 3D recognition algorithm based on spatio-temporal graph convolutional network in basketball pose estimation

2024

Int. J. Simul. Multidisci. Des. Optim.

View full text Add to dashboard Cite

In recent years, human motion recognition in computer vision has become a hot research direction in this field. Based on 2D human motion recognition technology, real-time extraction of motion features from 2D planes is used to recognize human movements. This method can only learn the position contour and color information of the image. It cannot directly reflect the motion situation, which results in low recognition accuracy and efficiency. In response to this issue, this study proposes a combination method of motion recognition and 3D pose estimation to recognize and classify basketball movements. First, the 2D skeleton model is obtained by extracting the feature information in the video action, which is converted into a 3D model, and the model is replaced by the time-space convolutional network to establish a human action recognition model. The experiment showed that when the number of iterations reached 6, the accuracy of the spatio-temporal graph convolutional network algorithm model reached 92%. Comparing the accuracy rates of different algorithm models, the average accuracy rates of convolutional neural network, long short memory network, graph convolution, long short model of action recognition and graph convolution model of action recognition were 61.6%, 65.4%, 72.5%, 76.8% and 90.3% respectively. The results show that the proposed 3D recognition algorithm can accurately recognize different basketball movements. This study can provide reference for basketball coaches and athletes in basketball training.

show abstract

“…6 Transfer learning has been proved to be an effective way to solve these problems and always generates higher accuracy and better training efficiency. 7,8 These transfer learning-based techniques can be divided into three categories: 9 (1) fully adaptive techniques, (2) partially adaptive techniques, and (3) zero adaptive techniques. In the fully adaptive techniques, all model weights are initialized by pre-training in the source domain and are updated during the training process in the target domain.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, due to the limitation of chest CT datasets, most of them cannot generate great results for clinical auxiliary detection 6 . Transfer learning has been proved to be an effective way to solve these problems and always generates higher accuracy and better training efficiency 7 , 8 …”

Section: Introductionmentioning

confidence: 99%

Targeted self-supervised attention network for coronavirus disease 2019 detection

Ding

2023

J. Electron. Imag.

View full text Add to dashboard Cite

.The worldwide spread of the coronavirus disease 2019 (COVID-19) has had a significant impact on healthcare systems globally. Timely and accurate diagnosis is crucial for controlling and preventing outbreaks. Computed tomography (CT) has been demonstrated as an effective diagnostic tool for COVID-19. Deep learning techniques have been applied in related fields to solve relevant problems. Normally, they achieve good performance on large datasets directly. However, due to the need to protect patient privacy and the sudden outbreak of the epidemic, the amount of publicly available CT data is insufficient for utilizing these methods. In addition, some natural images, which are used for model pre-training, are quite different from chest CT images, causing some inevitable errors. To address these issues, a targeted self-supervised attention network based on an improved inception ResNet called SIRANet is proposed. SIRANet incorporates a self-supervised learning method during the pre-training process and introduces a attention module to improve feature selection and focus on key informative features. Moreover, a transfer learning-based strategy for COVID-19 detection is presented and experiments are conducted on three widely used datasets, including ImageNet, an unlabeled chest CT dataset, and a COVID-19 CT dataset. Experimental results show that SIRANet outperforms other state-of-the-art techniques in terms of accuracy, precision, recall, and f1-score, demonstrating its effectiveness in capturing key informative features from unlabeled and labeled images. The implementation of SIRANet is available at https://github.com/Xiiin6/COVID-19.

show abstract

Bilayer model for cross-view human action recognition based on transfer learning

Cited by 5 publications

References 47 publications

[Retracted] Detection Method of Limb Movement in Competitive Sports Training Based on Deep Learning

[Retracted] Detection Method of Limb Movement in Competitive Sports Training Based on Deep Learning

Application of 3D recognition algorithm based on spatio-temporal graph convolutional network in basketball pose estimation

Targeted self-supervised attention network for coronavirus disease 2019 detection

Contact Info

Product

Resources

About