%0 Journal Article
	%A Yunyoung Nam and  Junghun Ryu and  Yoo-Joo Choi and  We-Duke Cho
	%D 2007
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 6, 2007
	%T Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People
	%U https://publications.waset.org/pdf/7797
	%V 6
	%X This paper presents a novel approach for representing
the spatio-temporal topology of the camera network with overlapping
and non-overlapping fields of view (FOVs). The topology is
determined by tracking moving objects and establishing object
correspondence across multiple cameras. To track people successfully
in multiple camera views, we used the Merge-Split (MS) approach for
object occlusion in a single camera and the grid-based approach for
extracting the accurate object feature. In addition, we considered the
appearance of people and the transition time between entry and exit
zones for tracking objects across blind regions of multiple cameras
with non-overlapping FOVs. The main contribution of this paper is to
estimate transition times between various entry and exit zones, and to
graphically represent the camera topology as an undirected weighted
graph using the transition probabilities.
	%P 1549 - 1554