  In recent years visual object tracking has become a very active research
area. An increasing number of tracking algorithms are being proposed each year.
It is because tracking has wide applications in various real world problems
such as human-computer interaction, autonomous vehicles, robotics, surveillance
and security just to name a few. In the current study, we review latest trends
and advances in the tracking area and evaluate the robustness of different
trackers based on the feature extraction methods. The first part of this work
comprises a comprehensive survey of the recently proposed trackers. We broadly
categorize trackers into Correlation Filter based Trackers (CFTs) and Non-CFTs.
Each category is further classified into various types based on the
architecture and the tracking mechanism. In the second part, we experimentally
evaluated 24 recent trackers for robustness, and compared handcrafted and deep
feature based trackers. We observe that trackers using deep features performed
better, though in some cases a fusion of both increased performance
significantly. In order to overcome the drawbacks of the existing benchmarks, a
new benchmark Object Tracking and Temple Color (OTTC) has also been proposed
and used in the evaluation of different algorithms. We analyze the performance
of trackers over eleven different challenges in OTTC, and three other
benchmarks. Our study concludes that Discriminative Correlation Filter (DCF)
based trackers perform better than the others. Our study also reveals that
inclusion of different types of regularizations over DCF often results in
boosted tracking performance. Finally, we sum up our study by pointing out some
insights and indicating future trends in visual object tracking field.
