loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Eighth IEEE Workshop on Applications of Computer Vision (WACV'07)
Homography-based Analysis of People and Vehicle Activities in Crowded Scenes
Austin, Texas
February 21-February 22
ISBN: 0-7695-2794-9
ASCII Text
x 
 
Sangho Park, Mohan M. Trivedi, "Homography-based Analysis of People and Vehicle Activities in Crowded Scenes," Applications of Computer Vision, IEEE Workshop on, pp. 51, Eighth IEEE Workshop on Applications of Computer Vision (WACV'07), 2007.
 
 
BibTex
x
 
@article{ 10.1109/WACV.2007.30,
author = {Sangho Park and Mohan M. Trivedi},
title = {Homography-based Analysis of People and Vehicle Activities in Crowded Scenes},
journal ={Applications of Computer Vision, IEEE Workshop on},
volume = {0},
year = {2007},
issn = {1550-5790},
pages = {51},
doi = {http://doi.ieeecomputersociety.org/10.1109/WACV.2007.30},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Applications of Computer Vision, IEEE Workshop on
TI - Homography-based Analysis of People and Vehicle Activities in Crowded Scenes
SN - 1550-5790
SP
EP
A1 - Sangho Park,
A1 - Mohan M. Trivedi,
PY - 2007
KW - null
VL - 0
JA - Applications of Computer Vision, IEEE Workshop on
ER -
 
Sangho Park, University of California, San Diego
Mohan M. Trivedi, University of California, San Diego
This paper presents an new framework for homographybased analysis of pedestrian-vehicle activity in crowded scenes. Planar homography constraint is exploited to extract view-invariant object features including footage area and velocity of objects on the ground plane. Spatiotemporal relationships between people- and vehicle- tracks are represented by a semantic event. Context awareness of the situation is achieved by the estimated density distribution of objects and the anticipation of possible directions of near-future tracks using piecewise velocity history. Singleview and multi-view based homography mapping options are compared. Our framework can be used to enhance situational awareness for disaster prevention, human interactions in structured environments, and crowd movement analysis at wide regions.
Citation:
Sangho Park, Mohan M. Trivedi, "Homography-based Analysis of People and Vehicle Activities in Crowded Scenes," wacv, pp.51, Eighth IEEE Workshop on Applications of Computer Vision (WACV'07), 2007
Usage of this product signifies your acceptance of the Terms of Use.