  High-resolution images can be used to resolve matching ambiguities between
trajectory fragments (tracklets), which is one of the main challenges in
multiple target tracking. A PTZ camera, which can pan, tilt and zoom, is a
powerful and efficient tool that offers both close-up views and wide area
coverage on demand. The wide-area view makes it possible to track many targets
while the close-up view allows individuals to be identified from
high-resolution images of their faces. A central component of a PTZ tracking
system is a scheduling algorithm that determines which target to zoom in on.
  In this paper we study this scheduling problem from a theoretical
perspective, where the high resolution images are also used for tracklet
matching. We propose a novel data structure, the Multi-Strand Tracking Graph
(MSG), which represents the set of tracklets computed by a tracker and the
possible associations between them. The MSG allows efficient scheduling as well
as resolving -- directly or by elimination -- matching ambiguities between
tracklets. The main feature of the MSG is the auxiliary data saved in each
vertex, which allows efficient computation while avoiding time-consuming graph
traversal. Synthetic data simulations are used to evaluate our scheduling
algorithm and to demonstrate its superiority over a na\"ive one.
