  Object trackers deployed on low-power devices need to be light-weight,
however, most of the current state-of-the-art (SOTA) methods rely on using
compute-heavy backbones built using CNNs or transformers. Large sizes of such
models do not allow their deployment in low-power conditions and designing
compressed variants of large tracking models is of great importance. This paper
demonstrates how highly compressed light-weight object trackers can be designed
using neural architectural pruning of large CNN and transformer based trackers.
Further, a comparative study on architectural choices best suited to design
light-weight trackers is provided. A comparison between SOTA trackers using
CNNs, transformers as well as the combination of the two is presented to study
their stability at various compression ratios. Finally results for extreme
pruning scenarios going as low as 1% in some cases are shown to study the
limits of network pruning in object tracking. This work provides deeper
insights into designing highly efficient trackers from existing SOTA methods.
