D. Yang, H. Gonzalez-Banos, and L. Guibas, Counting People in Crowds with a Real-Time Network of Image Sensors, Int. Conference on Computer Vision (ICCV), pp. 122-129, 2003.
Estimating the number of people in a crowded environment is a
central task in civilian surveillance. Most vision-based counting
techniques depend on detecting individuals in order to count, an
unrealistic proposition in crowded settings. We propose an
alternative approach that directly estimates the number of people.
In our system, groups of image sensors segment foreground objects
from the background, aggregate the resulting silhouettes over a
network, and compute a planar projection of the scene's visual
hull. We introduce a geometric algorithm that calculates bounds on
the number of persons in each region of the projection, after
phantom regions have been eliminated. The computational
requirements scale well with the number of sensors and the number
of people, and only limited amounts of data are transmitted over
the network. Because of these properties, our system runs in
real-time and can be deployed as an untethered wireless sensor
network. We describe the major components of our system, and
report preliminary experiments with our first prototype
, author = "D. Yang and H. Gonzalez-Banos and L. Guibas"
, title = "Counting People in Crowds with a Real-Time Network of Image Sensors"
, booktitle = "Proc. IEEE ICCV"
, year = 2003