Monocular multi-person pose estimation: A survey

Reis, Eduardo Souza dos; Seewald, Lucas Adams; Antunes, Rodolfo Stoffel; Rodrigues, Vinicius Facco; Righi, Rodrigo da Rosa; Costa, Cristiano André da; Silveira, Luiz Gonzaga da; Eskofier, Bjoern M.; Maier, Andreas; Horz, Tim; Fahrig, Rebecca

doi:10.1016/j.patcog.2021.108046

Cited by 14 publications

(12 citation statements)

References 10 publications

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“…Year Focus [138] 2020 Monocular 3D human pose estimation [139] 2021 Monocular multi-person pose estimation [140] 2021 3D human pose estimation algorithms for markerless motion capture [141] 2021 2D multi-person pose estimation methods [142] 2021 Deep 3D human pose estimation [143] 2021 Human pose estimation and its application to action recognition [144] 2022…”

Section: Referencesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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Section: Referencesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…[37] provided an overview of contemporary 2D pose estimation models, with a focus on architecture backbones, loss functions, and limitations. Multi-person pose estimation methods were the subject of investigation in [38,39], while [40] delved into deep models for 3D human pose estimation and offered comparisons of their strengths and weaknesses. Furthermore, Ref.…”

Section: Previous Surveysmentioning

confidence: 99%

A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation

El Kaid,

Baïna

2023

J. Imaging

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Three-dimensional human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject. For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information. Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses. Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation.

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“…Table 1 summarises the existing survey studies that review 2D and 3D HPE. The survey studies presented in [31,46,118,194] focus solely on 2D HPE, [56,70,155] focus on 3D HPE, and [25,69,93,168,220] address the hybrid models. It is also important to note that previous survey studies published before 2015, review more conventional approaches to HPE, whilst [25,31,220] survey present the deep learning-based approaches to HPE.…”

Section: Existing Survey Studiesmentioning

confidence: 99%

“…Table 1. A List of Existing Surveys on HPE along-with Brief Summary of Key Objectives Ref Year Description [168] 2021 A survey of 2D and 3D multi-person HPE. Includes a focus on the unique challenges of multi-person HPE such as occlusion and truncated body parts.…”

Section: Existing Survey Studiesmentioning

confidence: 99%

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Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models

et al. 2022

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Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need of high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeleton-based person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that make the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future.

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Monocular multi-person pose estimation: A survey

Cited by 14 publications

References 10 publications

A Survey on Artificial Intelligence in Posture Recognition

A Survey on Artificial Intelligence in Posture Recognition

A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation

Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models

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