Title: 
Tracking a Large Number of Objects from Multiple Views

Authors: 
Zheng Wu, Nickolay I. Hristov, Tyson L. Hedrick, Thomas H. Kunz, and
Margrit Betke

Date: March 18, 2009

Abstract:
We propose a multi-object multi-camera framework for tracking large
numbers of tightly-spaced objects that rapidly move in three
dimensions.  We formulate the problem of finding correspondences
across multiple views as a multidimensional assignment problem and use
a greedy randomized adaptive search procedure to solve this NP-hard
problem efficiently.  To account for occlusions, we relax the
one-to-one constraint that one measurement corresponds to one object
and iteratively solve the relaxed assignment problem.  After
correspondences are established, object trajectories are estimated by
stereoscopic reconstruction using an epipolar-neighborhood search.  We
embedded our method into a tracker-to-tracker multi-view fusion system
that not only obtains the three-dimensional trajectories of
closely-moving objects but also accurately settles track uncertainties
that could not be resolved from single views due to occlusion.  We
conducted experiments to validate our greedy assignment procedure and
our technique to recover from occlusions.  We successfully track
hundreds of flying bats and provide an analysis of their group
behavior based on 150 reconstructed 3D trajectories.