@InProceedings{Supelec259,
author = {Georges Adrian Drumea and Hervé Frezza-Buet},
title = {{Tracking fast changing non-stationary distributions with a topologically adaptive neural network: application to video tracking}},
year = {2007},
booktitle = {{ESANN, European Symposium on Artificial Neural Networks}},
pages = {43-48},
month = {apr},
address = {Bruges (Belgium)},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00250981/fr/},
abstract = {In this paper, an original method named GNG-T, extended from GNG-
U algorithm by Fritzke is presented. The method performs
continuously vector quantization over a distribution that
changes over time. It deals with both sudden changes and
continuous ones, and is thus suited for video tracking
framework, where continuous tracking is required as well as fast
adaptation to incoming and outgoing people. The central
mechanism relies on the management of quantization resolution,
that cope with stopping condition problems of usual Growing
Neural Gas inspired methods. Application to video tracking is
briefly presented.}
}