Monocular multi-person pose estimation: A survey

Dos Reis ES, Seewald LA, Antunes RS, Rodrigues VF, Righi RDR, Da Costa CA, Da Silveira Jr LG, Eskofier B, Maier A, Horz T, Fahrig R (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Pattern Recognition Elsevier

Book Volume: 118

DOI: 10.1016/j.patcog.2021.108046

Abstract

Multi-person pose estimation in unconstrained scenarios, with an unknown number of individuals, is a main step towards scene understanding and action recognition. Due to the recent advancements on the architecture of convolutional networks, body part detectors are now accurate and estimate poses in real-time for both single-and multi-person scenes. In contrast, assigning detected body parts to coherent human poses when there are multiple persons interacting is an arduous task. To name a few of the challenges that arise in such scenes: person-to-person occlusion, truncated body parts, and more sources for double counting. Recently, the community contributed towards solving most of them. Hence, it would be interesting to analyze and compile successful approaches from current literature into research trends, and identify possible gaps for future works. To the best of our knowledge, there is no up-to-date review on the main advancements in the field that target this particular set of challenges. This survey fills this gap by reviewing the main breakthroughs on multi-person pose estimation over the last decade and summarizing their impact on the state-of-the-art. Regarding scientific contributions, we propose a novel taxonomy that categorizes the reviewed methods according to their main contributions to the pose estimation pipeline, lists the main datasets and evaluation metrics to train new models, and provides insights on the best entries of publicly available benchmarks. (c) 2021 Elsevier Ltd. All rights reserved.

Authors with CRIS profile

Björn Eskofier Machine Learning and Data Analytics Lab Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Rebecca Fahrig Technische Fakultät

Involved external institutions

Unisinos (Universidade do Vale do Rio dos Sinos) BR Brazil (BR) Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Dos Reis, E.S., Seewald, L.A., Antunes, R.S., Rodrigues, V.F., Righi, R.D.R., Da Costa, C.A.,... Fahrig, R. (2021). Monocular multi-person pose estimation: A survey. Pattern Recognition, 118. https://dx.doi.org/10.1016/j.patcog.2021.108046

MLA:

Dos Reis, Eduardo Souza, et al. "Monocular multi-person pose estimation: A survey." Pattern Recognition 118 (2021).

BibTeX: Download