Comparative Analysis of OpenPose, PoseNet, and MoveNet Models for Pose Estimation in Mobile Devices

Jo, BeomJun; Kim, SeongKi

doi:10.18280/ts.390111

Cited by 45 publications

(15 citation statements)

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“…It shows good robustness in the pose estimation of one or more people. The input of OpenPose can realize pictures, videos, or real-time cameras as input and output the position and coordinate information of the joint points of the human body through OpenPose estimation [ 15 ]. The basic principle of OpenPose is to build a CNN in stages and then output the prediction confidence heat map of the skeleton points after predicting the human skeleton points in the image.…”

Section: Methodsmentioning

confidence: 99%

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model

Zhao

2022

Computational Intelligence and Neuroscience

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The purpose is to automatically and quickly analyze whether the rope skipping actions conform to the standards and give correct guidance and training plans. Firstly, aiming at the problem of motion analysis, a deep learning (DL) framework is proposed to obtain the coordinates of key points in rope skipping. The framework is based on the OpenPose method and uses the lightweight MobileNetV2 instead of the Visual Geometry Group (VGG) 19. Secondly, a multi-label classification model is proposed: attention long short-term memory-long short-term memory (ALSTM-LSTM), according to the algorithm adaptive method in the multi-label learning method. Finally, the validity of the model is verified. Through the analysis and comparison of simulation results, the results show that the average accuracy of the improved OpenPose method is 77.8%, an increase of 3.3%. The proposed ALSTM-LSTM model achieves 96.1% accuracy and 96.5% precision. After the feature extraction model VGG19 in the initial stage of OpenPose is replaced by the lightweight MobileNetV2, the pose estimation accuracy is improved, and the number of model parameters is reduced. Additionally, compared with other models, the performance of the ALSTM-LSTM model is improved in all aspects. This work effectively solves the problems of real-time and accurate analysis in human pose estimation (HPE). The simulation results show that the proposed DL model can effectively improve students’ high school entrance examination performance.

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Section: Methodsmentioning

confidence: 99%

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model

Zhao

2022

Computational Intelligence and Neuroscience

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“…DeepPose captures all joints and uses deep neural network regressors for pose estimation [114], and DensePose maps all human pixels from RGB image to its 3D body surface [115]. BeomJun et al [124] compared and analysed the major pose estimation frameworks. Pose-based methods for HAR use an explicit skeletal representation for motion description.…”

Section: Workflow Elementsmentioning

confidence: 99%

Intelligent Video Analytics for Human Action Recognition: The State of Knowledge

Kulbacki

Segen

Chaczko

et al. 2023

Sensors

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The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments.

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“…OpenPose, as a two-dimensional human pose estimation algorithm using a partial affinity domain, relied on a CNN and supervised learning to achieve human pose evaluation [24][25][26]. Like many bottom-up methods, firstly, OpenPose detects the joints (key points) of all people in the image and assigns the detected key points to each corresponding person.…”

Section: Recognition Of 2d Human Pose In Tennis Videomentioning

confidence: 99%

Analytical Model of Action Fusion in Sports Tennis Teaching by Convolutional Neural Networks

Li¹,

Guo

Huang

2022

Computational Intelligence and Neuroscience

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In order to improve the effectiveness of tennis teaching and enhance students’ understanding and mastery of tennis standard movements, based on the three-dimensional (3D) convolutional neural network architecture, the problem of action recognition is deeply studied. Firstly, through OpenPose, the recognition process of human poses in tennis sports videos is discussed. Athlete tracking algorithms are designed to target players. According to the target tracking data, combined with the movement characteristics of tennis, real-time semantic analysis is used to discriminate the movement types of human key point displacement in tennis. Secondly, through 2D pose estimation of tennis players, the analysis of tennis movement types is achieved. Finally, in the tennis player action recognition, a lightweight multiscale convolutional model is proposed for tennis player action recognition. Meanwhile, a key frame segment network (KFSN) for local information fusion based on keyframes is proposed. The network improves the efficiency of the whole action video learning. Through simulation experiments on the public dataset UCF101, the proposed 3DCNN-based KFSN achieves a recognition rate of 94.8%. The average time per iteration is only 1/3 of the C3D network, and the convergence speed of the model is significantly faster. The 3DCNN-based recognition method of information fusion action discussed can effectively improve the recognition effect of tennis actions and improve students’ learning and understanding of actions in the teaching process.

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Comparative Analysis of OpenPose, PoseNet, and MoveNet Models for Pose Estimation in Mobile Devices

Cited by 45 publications

References 0 publications

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model

Human Sports Action and Ideological and PoliticalEvaluation by Lightweight Deep Learning Model

Intelligent Video Analytics for Human Action Recognition: The State of Knowledge

Analytical Model of Action Fusion in Sports Tennis Teaching by Convolutional Neural Networks

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