Evidence map›Paper›PMID 38357450›Full record

ArticleData in brief2024

uB-VisioGeoloc: An image sequences dataset of pedestrian navigation including geolocalised-inertial information and spatial sound rendering of the urban environment's obstacles.

Florian Scalvini, Camille Bordeau, Maxime Ambard, Cyrille Migniot, Mathilde Vergnaud, Julien Dubois

Abstract read
In one paragraph

Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Florian ScalviniImViA EA 7535 - Université de Bourgogne, Dijon, France.
Camille BordeauLEAD CNRS UMR 5022, Université de Bourgogne, Dijon, France.
Maxime AmbardLEAD CNRS UMR 5022, Université de Bourgogne, Dijon, France.
Cyrille MigniotImViA EA 7535 - Université de Bourgogne, Dijon, France.
Mathilde VergnaudImViA EA 7535 - Université de Bourgogne, Dijon, France.
Julien DuboisImViA EA 7535 - Université de Bourgogne, Dijon, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dataset proposed is a collection of pedestrian navigation data sequences combining visual and spatial information. The pedestrian navigation sequences are situations encountered by a pedestrian walking in an urban outdoor environment, such as moving on the sidewalk, navigating through a crowd, or crossing a street when the pedestrian light traffic is green. The acquired data are timestamped provided RGB-D images and are associated with GPS, and inertial data (acceleration, rotation). These recordings were acquired by separate processes, avoiding delays during their capture to guarantee a synchronisation between the moment of acquisition by the sensor and the moment of recording on the system. The acquisition was made in the city of Dijon, France, including narrow streets, wide avenues, and parks. Annotations of the RGB-D are also provided by bounding boxes indicating the position of relevant static or dynamic objects present in a pedestrian area, such as a tree, bench, or person. This pedestrian navigation dataset aims to support the development of smart mobile systems to assist visually impaired people in their daily movements in an outdoor environment. In this context, the visual data and localisation sequences we provide can be used to elaborate the appropriate visual processing methods to extract relevant information about the obstacles and their current positions on the path. Alongside the dataset, a visual-to-auditory substitution method has been employed to convert each image sequence into corresponding stereophonic sound files, allowing for comparison and evaluation. Synthetic sequences associated with the same information set are also provided based on the recordings of a displacement within the 3D model of a real place in Dijon.

Indexed as

Artificial visionCamera RGB-DGPSIMUPedestrian navigationReal sceneSonificationVirtual scene

Identifiers

PMID38357450
PMCPMC10865199

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.