Review on Human Pose Estimation and Human Body Joints Localization

Desai, Miral M.; Mewada, Hiren

doi:10.12785/ijcds/100181

Cited by 5 publications

(3 citation statements)

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“…Once the visual input is received, we trained a model using inception v3. This system is capable of recognizing a variety of poses accurately [18]. In conclusion, we maximize the utilization of datasets to their fullest extent possible.…”

Section: Methodsmentioning

confidence: 94%

Advancing Yoga Practice : Leveraging Opencv and Mediapipe for Pose Detection

2024

IRJMETS

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Yoga is a centuries-old practice that has become more popular all over the world due to its benefits for the body, mind, and spirit [1]. Nowadays, yoga is a popular kind of fitness all around the world. Proper posture is the cornerstone of yoga. Despite all of yoga's health benefits, bad posture can cause problems including sprains and soreness in the muscles. Over the past few years, working remotely has drawn more interest from people than working in person. Our approach is beneficial to people who are used to living online and struggle to find the time to visit yoga facilities. An increasing number of people are interested in automating the analysis of yoga postures in order to improve practitioners' performance and knowledge of the poses as a result of technological integration. Our technique is used by the suggested system to accurately extract features and estimate pose from input photos or video frames. The model classifies the yoga positions using our system's web cameras, and the image is used as input. The asana is improved by enhancing the input derived from the yoga postures through the application of multiple deep learning models.

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Section: Methodsmentioning

confidence: 94%

Advancing Yoga Practice : Leveraging Opencv and Mediapipe for Pose Detection

2024

IRJMETS

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“…Therefore, feature extraction is necessary to distinguish the human pose from these other objects before estimating the position of the joints. Unlike traditional HPE methods, which explicitly extract features from an image using techniques such as HOG (histogram of oriented gradients), deep-learning-based HPE generates features implicitly as part of a CNN operation [69]. In HPE tasks, CNNs are used to extract features that represent the shape of the human body.…”

Section: Feature Extractionmentioning

confidence: 99%

Human Pose Estimation Using Deep Learning: A Systematic Literature Review

Samkari,

Arif,

Alghamdi

et al. 2023

MAKE

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Human Pose Estimation (HPE) is the task that aims to predict the location of human joints from images and videos. This task is used in many applications, such as sports analysis and surveillance systems. Recently, several studies have embraced deep learning to enhance the performance of HPE tasks. However, building an efficient HPE model is difficult; many challenges, like crowded scenes and occlusion, must be handled. This paper followed a systematic procedure to review different HPE models comprehensively. About 100 articles published since 2014 on HPE using deep learning were selected using several selection criteria. Both image and video data types of methods were investigated. Furthermore, both single and multiple HPE methods were reviewed. In addition, the available datasets, different loss functions used in HPE, and pretrained feature extraction models were all covered. Our analysis revealed that Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are the most used in HPE. Moreover, occlusion and crowd scenes remain the main problems affecting models’ performance. Therefore, the paper presented various solutions to address these issues. Finally, this paper highlighted the potential opportunities for future work in this task.

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“…Therefore, the mesh model is used to represent 3D pose estimation. The skeleton model represents body-joint localization in the proposed approach ( Desai & Mewada, 2021 ). The methodology to estimate human pose consists of the traditional method ( Yang & Ramanan, 2012 ) and the deep neural network-based approach.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for yoga pose estimation based on in-depth analysis of human body joint detection accuracy

Desai

Mewada

2023

PeerJ Computer Science

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Virtual motion and pose from images and video can be estimated by detecting body joints and their interconnection. The human body has diverse and complicated poses in yoga, making its classification challenging. This study estimates yoga poses from the images using a neural network. Five different yoga poses, viz. downdog, tree, plank, warrior2, and goddess in the form of RGB images are used as the target inputs. The BlazePose model was used to localize the body joints of the yoga poses. It detected a maximum of 33 body joints, referred to as keypoints, covering almost all the body parts. Keypoints achieved from the model are considered as predicted joint locations. True keypoints, as the ground truth body joint for individual yoga poses, are identified manually using the open source image annotation tool named Makesense AI. A detailed analysis of the body joint detection accuracy is proposed in the form of percentage of corrected keypoints (PCK) and percentage of detected joints (PDJ) for individual body parts and individual body joints, respectively. An algorithm is designed to measure PCK and PDJ in which the distance between the predicted joint location and true joint location is calculated. The experiment evaluation suggests that the adopted model obtained 93.9% PCK for the goddess pose. The maximum PCK achieved for the goddess pose, i.e., 93.9%, PDJ evaluation was carried out in the staggering mode where maximum PDJ is obtained as 90% to 100% for almost all the body joints.

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Review on Human Pose Estimation and Human Body Joints Localization

Cited by 5 publications

References 0 publications

Advancing Yoga Practice : Leveraging Opencv and Mediapipe for Pose Detection

Advancing Yoga Practice : Leveraging Opencv and Mediapipe for Pose Detection

Human Pose Estimation Using Deep Learning: A Systematic Literature Review

A novel approach for yoga pose estimation based on in-depth analysis of human body joint detection accuracy

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