Home News About Us Contact Contributors Disclaimer Privacy Policy Help FAQ

Home
Search
Quick Search
Advanced
Fulltext
Browse
Collections
Persons
My eDoc
Session History
Login
Name:
Password:
Documentation
Help
Support Wiki
Direct access to
document ID:


          Institute: MPI für Informatik     Collection: Computer Vision and Multimodal Computing     Display Documents



ID: 536686.0, MPI für Informatik / Computer Vision and Multimodal Computing
Vision Based Victim Detection from Unmanned Aerial Vehicles
Authors:Andriluka, Mykhaylo; Schnitzspan, Paul; Meyer, Johannes; Kohlbrecher, Stefan; Petersen, Karen; Stryk, Oskar von; Roth, Stefan; Schiele, Bernt
Language:English
Publisher:IEEE
Place of Publication:Piscataway, NJ
Date of Publication (YYYY-MM-DD):2010
Title of Proceedings:2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Start Page:1740
End Page:1747
Place of Conference/Meeting:Taipei, Taiwan
(Start) Date of Conference/Meeting
 (YYYY-MM-DD):
2010-10-18
End Date of Conference/Meeting 
 (YYYY-MM-DD):
2010-10-22
Audience:Experts Only
Intended Educational Use:No
Abstract / Description: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.
Last Change of the Resource (YYYY-MM-DD):2011-01-18
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Bernt Schiele
Affiliations:MPI für Informatik/Computer Vision and Multimodal Computing
Identifiers:LOCALID:C12576EE0048963A-87127B83B8CE0385C1257817004BECE4-...
URL:http://dx.doi.org/10.1109/IROS.2010.5649223
DOI:10.1109/IROS.2010.5649223
ISBN:978-1-4244-6674-0
The scope and number of records on eDoc is subject to the collection policies defined by each institute - see "info" button in the collection browse view.