Publication detail
Kinect-Supported Dataset Creation for Human Pose Estimation
BEHÚŇ, K. HEROUT, A. PÁLDY, A.
Original Title
Kinect-Supported Dataset Creation for Human Pose Estimation
English Title
Kinect-Supported Dataset Creation for Human Pose Estimation
Type
conference paper
Language
en
Original Abstract
Dataset creation of annotated human poses is very time-consuming. This paper presents an approach for rapid construction of a precisely annotated training dataset for human pose estimation of a sitting subject, intended especially for aeronautic cockpit. We propose to use Kinect as a tool for collecting ground truth to a purely visual dataset (for reasons defined by the application, use of Kinect or similar structured light-based approaches is impossible). Since Kinect annotation of individual joints might be imprecise at certain moments, manual post-processing of the acquired data is necessary and we propose a scheme for efficient and reliable manual post-annotation. We produced a dataset of 6,322 annotated frames, involving 11 human subjects recorded in various light condition, different clothing, and varying background. Each frame contains one seated person in frontal view with annotation of pose and optical flow data.
English abstract
Dataset creation of annotated human poses is very time-consuming. This paper presents an approach for rapid construction of a precisely annotated training dataset for human pose estimation of a sitting subject, intended especially for aeronautic cockpit. We propose to use Kinect as a tool for collecting ground truth to a purely visual dataset (for reasons defined by the application, use of Kinect or similar structured light-based approaches is impossible). Since Kinect annotation of individual joints might be imprecise at certain moments, manual post-processing of the acquired data is necessary and we propose a scheme for efficient and reliable manual post-annotation. We produced a dataset of 6,322 annotated frames, involving 11 human subjects recorded in various light condition, different clothing, and varying background. Each frame contains one seated person in frontal view with annotation of pose and optical flow data.
Keywords
Human Pose Estimation, Kinect RGBD, Visual Recognition
RIV year
2014
Released
28.05.2014
Publisher
Comenius University in Bratislava
Location
Smolenice
ISBN
978-80-223-3601-7
Book
Proceedings of Spring Conference on Computer Graphics
Edition
NEUVEDEN
Edition number
NEUVEDEN
Pages from
88
Pages to
95
Pages count
8
Documents
BibTex
@inproceedings{BUT111621,
author="Kamil {Behúň} and Adam {Herout} and Alexander {Páldy}",
title="Kinect-Supported Dataset Creation for Human Pose Estimation",
annote="Dataset creation of annotated human poses is very time-consuming. This paper
presents an approach for rapid construction of a precisely annotated training
dataset for human pose estimation of a sitting subject, intended especially for
aeronautic cockpit. We propose to use Kinect as a tool for collecting ground
truth to a purely visual dataset (for reasons defined by the application, use of
Kinect or similar structured light-based approaches is impossible). Since Kinect
annotation of individual joints might be imprecise at certain moments, manual
post-processing of the acquired data is necessary and we propose a scheme for
efficient and reliable manual post-annotation. We produced a dataset of 6,322
annotated frames, involving 11 human subjects recorded in various light
condition, different clothing, and varying background. Each frame contains one
seated person in frontal view with annotation of pose and optical flow data.",
address="Comenius University in Bratislava",
booktitle="Proceedings of Spring Conference on Computer Graphics",
chapter="111621",
doi="10.1145/2643188.2643195",
edition="NEUVEDEN",
howpublished="print",
institution="Comenius University in Bratislava",
year="2014",
month="may",
pages="88--95",
publisher="Comenius University in Bratislava",
type="conference paper"
}