Visual–Inertial Fusion-Based Human Pose Estimation: A Review

Li, Tong; Huang, Yu

doi:10.1109/tim.2023.3286000

Cited by 6 publications

(1 citation statement)

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“…Similarly, 3D HPE performs the same task as 2D HPE but also considers the Z-axis (or depth information). Both 2D and 3D HPE face challenges [10]. Nevertheless, the accuracy of 3D HPE heavily relies on 2D HPE [11], as obtaining a 2D location for each human joint is the first step in 3D HPE.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%