  Visual object tracking is an important computer vision problem with numerous
real-world applications including human-computer interaction, autonomous
vehicles, robotics, motion-based recognition, video indexing, surveillance and
security. In this paper, we aim to extensively review the latest trends and
advances in the tracking algorithms and evaluate the robustness of trackers in
the presence of noise. The first part of this work comprises a comprehensive
survey of recently proposed tracking algorithms. We broadly categorize trackers
into correlation filter based trackers and the others as non-correlation filter
trackers. Each category is further classified into various types of trackers
based on the architecture of the tracking mechanism. In the second part of this
work, we experimentally evaluate tracking algorithms for robustness in the
presence of additive white Gaussian noise. Multiple levels of additive noise
are added to the Object Tracking Benchmark (OTB) 2015, and the precision and
success rates of the tracking algorithms are evaluated. Some algorithms
suffered more performance degradation than others, which brings to light a
previously unexplored aspect of the tracking algorithms. The relative rank of
the algorithms based on their performance on benchmark datasets may change in
the presence of noise. Our study concludes that no single tracker is able to
achieve the same efficiency in the presence of noise as under noise-free
conditions; thus, there is a need to include a parameter for robustness to
noise when evaluating newly proposed tracking algorithms.
