Dataset Open Access

UNICITY: A depth maps database for people detection in security airlocks

Joël Dumoulin; Olivier Canévet; Michael Villamizar; Hugo Nunes; Omar Abou Khaled; Elena Mugellini; Fabrice Moscheni; Jean-Marc Odobez

BibTeX Export

@misc{joel_dumoulin_2019_2556679,
  author       = {Joël Dumoulin and
                  Olivier Canévet and
                  Michael Villamizar and
                  Hugo Nunes and
                  Omar Abou Khaled and
                  Elena Mugellini and
                  Fabrice Moscheni and
                  Jean-Marc Odobez},
  title        = {{UNICITY: A depth maps database for people 
                   detection in security airlocks}},
  month        = feb,
  year         = 2019,
  doi          = {10.5281/zenodo.2556679},
  url          = {https://doi.org/10.5281/zenodo.2556679}
}
412
399
views
downloads
All versions This version
Views 412412
Downloads 399399
Data volume 4.6 TB4.6 TB
Unique views 375375
Unique downloads 7878
More info on how stats are collected.
Indexed in
Publication date:
February 4, 2019
DOI:
10.5281/zenodo.2556679

Zenodo DOI Badge

DOI 

10.5281/zenodo.2556679

Markdown 

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2556679.svg)](https://doi.org/10.5281/zenodo.2556679)

reStructedText 

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2556679.svg
   :target: https://doi.org/10.5281/zenodo.2556679

HTML 

<a href="https://doi.org/10.5281/zenodo.2556679"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.2556679.svg" alt="DOI"></a>

Image URL 

https://zenodo.org/badge/DOI/10.5281/zenodo.2556679.svg

Target URL 

https://doi.org/10.5281/zenodo.2556679

Keyword(s):
unicity dataset depth images computer vision machine learning people detection video surveillance
Meeting:
International Conference on Advanced Video and Signal-based Surveillance (AVSS), Auckland, New Zealand, 27-30 November 2018 (Session Workshop )
License (for files):
Creative Commons Attribution 4.0 International

Versions

Version 1 10.5281/zenodo.2556679 Feb 4, 2019
Cite all versions? You can cite all versions by using the DOI 10.5281/zenodo.2556678. This DOI represents all versions, and will always resolve to the latest one. Read more.

Share

Cite as

Export