Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Computational Intelligence: Collaboration, Fusion and Emergence |
| Editors | Christine Mumford, Lakhmi Jain |
| Publisher | Springer |
| Pages | 464-483 |
| Volume | 1 |
| ISBN (Print) | 978-3-642-01798-8 |
| Publication status | Published - 21 Jul 2009 |
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Cite this
Zhan, B., Remagnino, P., Monekosso, D., & Velastin, S. (2009). The Analysis of Crowd Dynamics: from Observations to Modeling. In C. Mumford, & L. Jain (Eds.), Computational Intelligence: Collaboration, Fusion and Emergence (Vol. 1, pp. 464-483). Springer.