Abstract
A neural-based crowd estimation system for surveillance in complex scenes at underground station platform is presented. Estimation is carried out by extracting a set of significant features from sequences of images. Those feature indexes are modeled by a neural network to estimate the crowd density. The learning phase is based on our proposed hybrid of the least-squares and global search algorithms which are capable of providing the global search characteristic and fast convergence speed. Promising experimental results are obtained in terms of accuracy and real-time response capability to alert operators automatically.
| Original language | English |
|---|---|
| Pages (from-to) | 535-541 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |
| Volume | 29 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Aug 1999 |
| Externally published | Yes |
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering