DocumentCode
1039904
Title
DEKF system for crowding estimation by a multiple-model approach
Author
Tesei, Anna
Volume
30
Issue
5
fYear
1994
fDate
3/3/1994 12:00:00 AM
Firstpage
390
Lastpage
391
Abstract
A distributed extended Kalman filter (DEKF) network devoted to real-time crowding estimation for surveillance in complex scenes is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Feature values are associated by virtual sensors with the estimated number of people using nonlinear models obtained in an off-line training phase. Different models are used, depending on the positions and dimensions of the crowded subareas detected in each image
Keywords
Kalman filters; feature extraction; image sequences; DEKF system; complex scenes; crowded subareas; distributed extended Kalman filter; feature values; image sequences; multiple-model approach; nonlinear models; off-line training phase; real-time crowding estimation; significant features; surveillance; virtual sensors;
fLanguage
English
Journal_Title
Electronics Letters
Publisher
iet
ISSN
0013-5194
Type
jour
DOI
10.1049/el:19940280
Filename
273277
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