  In this paper, a unified three-layer hierarchical approach for solving
tracking problems in multiple non-overlapping cameras is proposed. Given a
video and a set of detections (obtained by any person detector), we first solve
within-camera tracking employing the first two layers of our framework and,
then, in the third layer, we solve across-camera tracking by merging tracks of
the same person in all cameras in a simultaneous fashion. To best serve our
purpose, a constrained dominant sets clustering (CDSC) technique, a
parametrized version of standard quadratic optimization, is employed to solve
both tracking tasks. The tracking problem is caste as finding constrained
dominant sets from a graph. In addition to having a unified framework that
simultaneously solves within- and across-camera tracking, the third layer helps
link broken tracks of the same person occurring during within-camera tracking.
In this work, we propose a fast algorithm, based on dynamics from evolutionary
game theory, which is efficient and salable to large-scale real-world
applications.
