Human keypoint detection for close proximity human-robot interaction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00362006" target="_blank" >RIV/68407700:21230/22:00362006 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/Humanoids53995.2022.10000133" target="_blank" >https://doi.org/10.1109/Humanoids53995.2022.10000133</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/Humanoids53995.2022.10000133" target="_blank" >10.1109/Humanoids53995.2022.10000133</a>

Result language

angličtina

Original language name

Human keypoint detection for close proximity human-robot interaction

Original language description

We study the performance of state-of-the-art human keypoint detectors in the context of close proximity human-robot interaction. The detection in this scenario is specific in that only a subset of body parts such as hands and torso are in the field of view. In particular, (i) we survey existing datasets with human pose annotation from the perspective of close proximity images and prepare and make publicly available a new Human in Close Proximity (HiCP) dataset; (ii) we quantitatively and qualitatively compare state-of-the-art human whole-body 2D keypoint detection methods (OpenPose, MMPose, AlphaPose, Detectron2) on this dataset; (iii) since accurate detection of hands and fingers is critical in applications with handovers, we evaluate the performance of the MediaPipe hand detector; (iv) we deploy the algorithms on a humanoid robot with an RGB-D camera on its head and evaluate the performance in 3D human keypoint detection. A motion capture system is used as reference. The best performing whole-body keypoint detectors in close proximity were MMPose and AlphaPose, but both had difficulty with finger detection. Thus, we propose a combination of MMPose or AlphaPose for the body and MediaPipe for the hands in a single framework providing the most accurate and robust detection. We also analyse the failure modes of individual detectors---for example, to what extent the absence of the head of the person in the image degrades performance. Finally, we demonstrate the framework in a scenario where a humanoid robot interacting with a person uses the detected 3D keypoints for whole-body avoidance maneuvers.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20204 - Robotics and automatic control

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2022 IEEE-RAS International Conference on Humanoid Robots (Humanoids)

ISBN

979-8-3503-0979-9

ISSN

2164-0572

e-ISSN

2164-0580

Number of pages

8

Pages from-to

450-457

Publisher name

IEEE

Place of publication

Piscataway, NJ

Event location

Okinawa

Event date

Nov 28, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000925894300059