Special Issue "Computer Vision Datasets for Positioning, Tracking and Wayfinding"

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: 30 November 2022 | Viewed by 618

Special Issue Editors

Dr. Filipe Meneses
E-Mail Website
Guest Editor
Information Systems Department, University of Minho, Campus Azurém, 4800-058 Guimarães, Portugal
Interests: indoor localization; ubiquitous computing; mobile computing; pervasive; location
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although Global Navigation Satellite Systems (GNSSs) are widely used for positioning purposes, they cannot properly operate in indoor areas such as hospitals, shopping areas, factories, or warehouses. Despite most indoor positioning solutions depending on radio frequency, there is rising interest in positioning using computer vision, as this methodology may be an interesting, cost-effective alternative to well-known systems based on LiDAR or UWB.

This Special Issue encourages authors from academia and industry to submit their datasets for positioning, tracking, and wayfinding using computer vision. The Special Issue topics include, but are not limited to:

  • Computer vision datasets;
  • Multi-source datasets, including sensor fusion;
  • Multi-range datasets.

Co-submissions that provide additional content to merit a new paper based on the published research articles are also welcome. These may include updates to a reported dataset, fuller release of a dataset, and useful information to enhance data transparency or reusability.

Dr. Filipe Meneses
Dr. Joaquín Torres-Sospedra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Data Descriptor
UNIPD-BPE: Synchronized RGB-D and Inertial Data for Multimodal Body Pose Estimation and Tracking
Data 2022, 7(6), 79; https://doi.org/10.3390/data7060079 - 09 Jun 2022
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Abstract
The ability to estimate human motion without requiring any external on-body sensor or marker is of paramount importance in a variety of fields, ranging from human–robot interaction, Industry 4.0, surveillance, and telerehabilitation. The recent development of portable, low-cost RGB-D cameras pushed forward the [...] Read more.
The ability to estimate human motion without requiring any external on-body sensor or marker is of paramount importance in a variety of fields, ranging from human–robot interaction, Industry 4.0, surveillance, and telerehabilitation. The recent development of portable, low-cost RGB-D cameras pushed forward the accuracy of markerless motion capture systems. However, despite the widespread use of such sensors, a dataset including complex scenes with multiple interacting people, recorded with a calibrated network of RGB-D cameras and an external system for assessing the pose estimation accuracy, is still missing. This paper presents the University of Padova Body Pose Estimation dataset (UNIPD-BPE), an extensive dataset for multi-sensor body pose estimation containing both single-person and multi-person sequences with up to 4 interacting people. A network with 5 Microsoft Azure Kinect RGB-D cameras is exploited to record synchronized high-definition RGB and depth data of the scene from multiple viewpoints, as well as to estimate the subjects’ poses using the Azure Kinect Body Tracking SDK. Simultaneously, full-body Xsens MVN Awinda inertial suits allow obtaining accurate poses and anatomical joint angles, while also providing raw data from the 17 IMUs required by each suit. This dataset aims to push forward the development and validation of multi-camera markerless body pose estimation and tracking algorithms, as well as multimodal approaches focused on merging visual and inertial data. Full article
(This article belongs to the Special Issue Computer Vision Datasets for Positioning, Tracking and Wayfinding)
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