Abstract
We discuss the problem of detecting dominant motions
in dense crowds, a challenging and societally important
problem. First, we survey the general literature of computer vision
algorithms that deal with crowds of people, including model- and
feature-based approaches to segmentation and tracking as well
as algorithms that analyze general motion trends. Second, we
present a system for automatically identifying dominant motions
in a crowded scene. Accurately tracking individual objects in such
scenes is difficult due to inter- and intra-object occlusions that
cannot be easily resolved. Our approach begins by independently
tracking low-level features using optical flow. While many of the
feature point tracks are unreliable, we show that they can be
clustered into smooth dominant motions using a distance measure
for feature trajectories based on longest common subsequences.
Results on real video sequences demonstrate that the approach
can successfully identify both dominant and anomalous motions in
crowded scenes. These fully-automatic algorithms could be easily
incorporated into distributed camera networks for autonomous
scene analysis.