  Full body trackers are utilized for surveillance and security purposes, such
as person-tracking robots. In the Middle East, uniform crowd environments are
the norm which challenges state-of-the-art trackers. Despite tremendous
improvements in tracker technology documented in the past literature, these
trackers have not been trained using a dataset that captures these
environments. In this work, we develop an annotated dataset with one specific
target per video in a uniform crowd environment. The dataset was generated in
four different scenarios where mainly the target was moving alongside the
crowd, sometimes occluding with them, and other times the camera's view of the
target is blocked by the crowd for a short period. After the annotations, it
was used in evaluating and fine-tuning a state-of-the-art tracker. Our results
have shown that the fine-tuned tracker performed better on the evaluation
dataset based on two quantitative evaluation metrics, compared to the initial
pre-trained tracker.
