  Object tracking becomes critical especially when similar objects are present
in the same area. Recent state-of-the-art (SOTA) approaches are proposed based
on taking a matching network with a heavy structure to distinguish the target
from other objects in the area which indeed drastically downgrades the
performance of the tracker in terms of speed. Besides, several candidates are
considered and processed to localize the intended object in a region of
interest for each frame which is time-consuming. In this article, a special
architecture is proposed based on which in contrast to the previous approaches,
it is possible to identify the object location in a single shot while taking
its template into account to distinguish it from the similar objects in the
same area. In brief, first of all, a window containing the object with twice
the target size is considered. This window is then fed into a fully
convolutional neural network (CNN) to extract a region of interest (RoI) in a
form of a matrix for each of the frames. In the beginning, a template of the
target is also taken as the input to the CNN. Considering this RoI matrix, the
next movement of the tracker is determined based on a simple and fast method.
Moreover, this matrix helps to estimate the object size which is crucial when
it changes over time. Despite the absence of a matching network, the presented
tracker performs comparatively with the SOTA in challenging situations while
having a super speed compared to them (up to $120 FPS$ on 1080ti). To
investigate this claim, a comparison study is carried out on the GOT-10k
dataset. Results reveal the outstanding performance of the proposed method in
fulfilling the task.
