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Proceedings Article, Paper
@InProceedings
Beitrag in Tagungsband, Workshop

Author, Editor
Author(s):
Andriluka, Mykhaylo
Schnitzspan, Paul
Meyer, Johannes
Kohlbrecher, Stefan
Petersen, Karen
Stryk, Oskar von
Roth, Stefan
Schiele, Bernt		dblp
dblp
dblp
dblp
dblp
dblp
dblp
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Not MPG Author(s):
Andriluka, Mykhaylo
Schnitzspan, Paul
Meyer, Johannes
Kohlbrecher, Stefan
Petersen, Karen
Stryk, Oskar von
Roth, Stefan
Editor(s):
BibTeX cite key*:
AndrilukaIROS2010
Title, Booktitle
Title*:
Vision Based Victim Detection from Unmanned Aerial Vehicles
Booktitle*:
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Event, URLs
Conference URL::
Downloading URL:
http://dx.doi.org/10.1109/IROS.2010.5649223
Event Address*:
Taipei, Taiwan
Language:
English
Event Date*
(no longer used):
Organization:
Event Start Date:
18 October 2010
Event End Date:
22 October 2010
Publisher
Name*:
IEEE
URL:
Address*:
Piscataway, NJ
Type:
Vol, No, Year, pp.
Series:
Volume:
Number:
Month:
Pages:
1740-1747
Year*:
2010
VG Wort Pages:
ISBN/ISSN:
978-1-4244-6674-0
Sequence Number:
DOI:
10.1109/IROS.2010.5649223
Note, Abstract, ©
(LaTeX) Abstract:
Finding injured humans is one of the primary
goals of any search and rescue operation. The aim of this paper
is to address the task of automatically finding people lying on
the ground in images taken from the on-board camera of an
unmanned aerial vehicle (UAV).
In this paper we evaluate various state-of-the-art visual
people detection methods in the context of vision based victim
detection from an UAV. The top performing approaches in
this comparison are those that rely on flexible part-based
representations and discriminatively trained part detectors. We
discuss their strengths and weaknesses and demonstrate that by
combining multiple models we can increase the reliability of the
system. We also demonstrate that the detection performance
can be substantially improved by integrating the height and
pitch information provided by on-board sensors. Jointly these
improvements allow us to significantly boost the detection
performance over the current de-facto standard, which provides
a substantial step towards making autonomous victim detection
for UAVs practical.
Download
Access Level:
Public

Correlation
MPG Unit:
Max-Planck-Institut für Informatik
MPG Subunit:
Computer Vision and Multimodal Computing
Appearance:
MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{AndrilukaIROS2010,
AUTHOR = {Andriluka, Mykhaylo and Schnitzspan, Paul and Meyer, Johannes and Kohlbrecher, Stefan and Petersen, Karen and Stryk, Oskar von and Roth, Stefan and Schiele, Bernt},
 TITLE = {Vision Based Victim Detection from Unmanned Aerial Vehicles},
 BOOKTITLE = {2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 PUBLISHER = {IEEE},
 YEAR = {2010},
 PAGES = {1740--1747},
 ADDRESS = {Taipei, Taiwan},
 ISBN = {978-1-4244-6674-0},
 DOI = {10.1109/IROS.2010.5649223},
}

Entry last modified by Anja Becker, 01/18/2011
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Editor(s)
[Library]		Created
01/13/2011 14:49:18
Revision
1.
0.


Editor
Anja Becker
Sandra Ebert


Edit Date
18.01.2011 14:17:12
01/13/2011 02:49:18 PM