A neural-based crowd estimation by hybrid global learning algorithm.
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.
Cho
S Y
SY
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon.
Chow
T S
TS
Leung
C T
CT