  In this paper, we propose a pipeline for multi-target visual tracking under
multi-camera system. For multi-camera system tracking problem, efficient data
association across cameras, and at the same time, across frames becomes more
important than single-camera system tracking. However, most of the multi-camera
tracking algorithms emphasis on single camera across frame data association.
Thus in our work, we model our tracking problem as a global graph, and adopt
Generalized Maximum Multi Clique optimization problem as our core algorithm to
take both across frame and across camera data correlation into account all
together. Furthermore, in order to compute good similarity scores as the input
of our graph model, we extract both appearance and dynamic motion similarities.
For appearance feature, Local Maximal Occurrence Representation(LOMO) feature
extraction algorithm for ReID is conducted. When it comes to capturing the
dynamic information, we build Hankel matrix for each tracklet of target and
apply rank estimation with Iterative Hankel Total Least Squares(IHTLS)
algorithm to it. We evaluate our tracker on the challenging Terrace Sequences
from EPFL CVLAB as well as recently published Duke MTMC dataset.
