  Recent progresses in visual tracking have greatly improved the tracking
performance. However, challenges such as occlusion and view change remain
obstacles in real world deployment. A natural solution to these challenges is
to use multiple cameras with multiview inputs, though existing systems are
mostly limited to specific targets (e.g. human), static cameras, and/or camera
calibration. To break through these limitations, we propose a generic multiview
tracking (GMT) framework that allows camera movement, while requiring neither
specific object model nor camera calibration. A key innovation in our framework
is a cross-camera trajectory prediction network (TPN), which implicitly and
dynamically encodes camera geometric relations, and hence addresses missing
target issues such as occlusion. Moreover, during tracking, we assemble
information across different cameras to dynamically update a novel
collaborative correlation filter (CCF), which is shared among cameras to
achieve robustness against view change. The two components are integrated into
a correlation filter tracking framework, where the features are trained offline
using existing single view tracking datasets. For evaluation, we first
contribute a new generic multiview tracking dataset (GMTD) with careful
annotations, and then run experiments on GMTD and the PETS2009 datasets. On
both datasets, the proposed GMT algorithm shows clear advantages over
state-of-the-art ones.
