  Obtained by moving object detection, the foreground mask result is unshaped
and can not be directly used in most subsequent processes. In this paper, we
focus on this problem and address it by constructing an optical flow based
moving foreground analysis framework. During the processing procedure, the
foreground masks are analyzed and segmented through two complementary
clustering algorithms. As a result, we obtain the instance-level information
like the number, location and size of moving objects. The experimental result
show that our method adapts itself to the problem and performs well enough for
practical applications.
