  Object tracking in realistic scenarios is a difficult problem affected by
various image factors such as occlusion, clutter, confusion, object shape,
unstable speed, and zooming. While these conditions do affect tracking
performance, there is no clear distinction between the scene dependent
challenges like occlusion, clutter, etc., and the challenges imposed by
traditional notions of impairments from capture, compression, processing, and
transmission. This paper is concerned with the latter interpretation of quality
as it affects video tracking performance. In this work we aim to evaluate two
state-of-the-art trackers (STRUCK and TLD) systematically and experimentally in
surveillance videos affected by in-capture distortions such as under-exposure
and defocus. We evaluate these trackers with the area under curve (AUC) values
of success plots and precision curves. In spite of the fact that STRUCK and TLD
have ranked high in video tracking surveys. This study concludes that incapture
distortions severely affect the performance of these trackers. For this reason,
the design and construction of a robust tracker with respect to these
distortions remains an open question that can be answered by creating
algorithms that makes use of perceptual features to compensate the degradations
provided by these distortions.
