Activity recognition using ST-GCN with 3D motion data

Cao, Xin; Kudo, Wataru; Ito, Chihiro; Shimizu, Masaki; Maeda, Eriko

doi:10.1145/3341162.3345581

Cited by 20 publications

(7 citation statements)

References 6 publications

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“…The results in Figure 11 demonstrate that the model's accuracy in this study reached 96.6% on the dataset, respectively, which is significantly better than the benchmark method based on ST-GCN in literature [ 15 ] with 88.4%. Compared with other excellent frontier methods, it is also relatively competitive.…”

Section: Results Analysis and Discussionmentioning

confidence: 64%

“…In addition, the coordinates of key points, angles, and camera views are important information, and different forms of input data have a significant impact on the model accuracy. The ST-GCN model is the first to apply graph-based convolutional networks to skeleton-based action recognition [ 15 ]. Literature [ 16 ] adds an attention (attention) module to the graph convolutional network layer to help the network pay more attention to the input data's important points, frames, and features.…”

Section: Introductionmentioning

confidence: 99%

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Action Recognition Model in Choral Conducting Teaching in Colleges and Universities under New Media Environment

Jiao

2022

Computational Intelligence and Neuroscience

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In current college music education, choral conducting is a required course for students. The course implementation aims to cultivate excellent and high-quality choral conductors. The requirements for choral conducting teaching in college music education under the new media environment have been further improved. First, this study gives the value of applying new media technology in choral conducting teaching in colleges and universities. Then, based on the key point that choral conductors’ expression of music mainly relies on gestural language, an action recognition model in college choral conducting teaching is proposed. The model is designed with an adaptive deep graph convolution model, and a spatio-temporal convolution submodel with a small number of parameters is created using group convolution. After the trained teacher model is obtained, the spatio-temporal convolutional submodel with fewer parameters is trained using the knowledge distillation method combined with data augmentation techniques. The final action recognition fusion model is obtained using the linear fusion method. The experimental results demonstrate that the proposed model can recognize the movements in college choral conducting teaching with higher performance than other existing models, which provides effective guidance for college choral conducting teaching in the new media environment.

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Section: Results Analysis and Discussionmentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

Action Recognition Model in Choral Conducting Teaching in Colleges and Universities under New Media Environment

Jiao

2022

Computational Intelligence and Neuroscience

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“…It has been stated that for 3D motion data the satisfactory results for activity recognition are obtained using a Spatial-Temporal Graph Convolutional Network [42,43]. In this paper, this network was implemented to recognise tennis forehand and backhand shots based on images that represent a model of an athlete together with a tennis racket.…”

Section: Discussionmentioning

confidence: 99%

Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks

Skublewska‐Paszkowska

Powroźnik

Lukasik

2020

Sensors

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Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.

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“…Recently, the ST-GCN model usage has increased considerably. From stroke type recognition in tennis [28], nurse activity recognition in hospitals [29], stock price prediction [30], fall detection [31] and action recognition systems [32]. Unfortunately, this architecture presents some disadvantages.…”

Section: Related Workmentioning

confidence: 99%

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

Alsawadi¹,

El-kenawy²,

Rio³

2023

Computers, Materials &Amp; Continua

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The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field. Therefore, finding efficient solutions to extract knowledge from these sources is imperative. Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices. With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action. We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks. Hence, in this study, we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition. Moreover, we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor. Additionally, we propose different skeleton detection thresholds that can improve the accuracy performance even further. We reached a top-1 accuracy performance of 40.1% on the Kinetics dataset. For the NTU-RGB+D dataset, we achieved 87.59% and 92.1% accuracy for Cross-Subject and Cross-View evaluation criteria, respectively.

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Activity recognition using ST-GCN with 3D motion data

Cited by 20 publications

References 6 publications

Action Recognition Model in Choral Conducting Teaching in Colleges and Universities under New Media Environment

Action Recognition Model in Choral Conducting Teaching in Colleges and Universities under New Media Environment

Learning Three Dimensional Tennis Shots Using Graph Convolutional Networks

Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

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