Abstract
| Language | English |
|---|---|
| Title of host publication | Computational Intelligence: Collaboration, Fusion and Emergence |
| Editors | Christine Mumford, Lakhmi Jain |
| Pages | 464-483 |
| Volume | 1 |
| Publication status | Published - 21 Jul 2009 |
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The Analysis of Crowd Dynamics: from Observations to Modeling. / Zhan, Beibei; Remagnino, Paolo; Monekosso, Dorothy; Velastin, Sergio.
Computational Intelligence: Collaboration, Fusion and Emergence. ed. / Christine Mumford; Lakhmi Jain. Vol. 1 2009. p. 464-483.Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - The Analysis of Crowd Dynamics: from Observations to Modeling
AU - Zhan, Beibei
AU - Remagnino, Paolo
AU - Monekosso, Dorothy
AU - Velastin, Sergio
PY - 2009/7/21
Y1 - 2009/7/21
N2 - Crowd is a familiar phenomenon studied in a variety of research disciplines including sociology, civil engineering and physics. Over the last two decades computer vision has become increasingly interested in studying crowds and their dynamics: because the phenomenon is of great scientific interest, it offers new computational challenges and because of a rapid increase in video surveillance technology deployed in public and private spaces. In this chapter computer vision techniques, combined with statistical methods and neural network, are used to automatically observe measure and learn crowd dynamics. The problem is studied to offer methods to measure crowd dynamics and model the complex movements of a crowd. The refined matching of local descriptors is used to measure crowd motion and statistical analysis and a kind of neural network, self-organizing maps were employed to learn crowd dynamics models.
AB - Crowd is a familiar phenomenon studied in a variety of research disciplines including sociology, civil engineering and physics. Over the last two decades computer vision has become increasingly interested in studying crowds and their dynamics: because the phenomenon is of great scientific interest, it offers new computational challenges and because of a rapid increase in video surveillance technology deployed in public and private spaces. In this chapter computer vision techniques, combined with statistical methods and neural network, are used to automatically observe measure and learn crowd dynamics. The problem is studied to offer methods to measure crowd dynamics and model the complex movements of a crowd. The refined matching of local descriptors is used to measure crowd motion and statistical analysis and a kind of neural network, self-organizing maps were employed to learn crowd dynamics models.
M3 - Chapter
SN - 978-3-642-01798-8
VL - 1
SP - 464
EP - 483
BT - Computational Intelligence: Collaboration, Fusion and Emergence
A2 - Mumford, Christine
A2 - Jain, Lakhmi
ER -