  Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in
computer vision for many years. Compared to tracking with a still camera, the
images captured with a PTZ camera are highly dynamic in nature because the
camera can perform large motion resulting in quickly changing capture
conditions. Furthermore, tracking with a PTZ camera involves camera control to
position the camera on the target. For successful tracking and camera control,
the tracker must be fast enough, or has to be able to predict accurately the
next position of the target. Therefore, standard benchmarks do not allow to
assess properly the quality of a tracker for the PTZ scenario. In this work, we
use a virtual PTZ framework to evaluate different tracking algorithms and
compare their performances. We also extend the framework to add target position
prediction for the next frame, accounting for camera motion and processing
delays. By doing this, we can assess if predicting can make long-term tracking
more robust as it may help slower algorithms for keeping the target in the
field of view of the camera. Results confirm that both speed and robustness are
required for tracking under the PTZ scenario.
