  Single object tracking is a vital task of many applications in critical
fields. However, it is still considered one of the most challenging vision
tasks. In recent years, computer vision, especially object tracking, witnessed
the introduction or adoption of many novel techniques, setting new fronts for
performance. In this survey, we visit some of the cutting-edge techniques in
vision, such as Sequence Models, Generative Models, Self-supervised Learning,
Unsupervised Learning, Reinforcement Learning, Meta-Learning, Continual
Learning, and Domain Adaptation, focusing on their application in single object
tracking. We propose a novel categorization of single object tracking methods
based on novel techniques and trends. Also, we conduct a comparative analysis
of the performance reported by the methods presented on popular tracking
benchmarks. Moreover, we analyze the pros and cons of the presented approaches
and present a guide for non-traditional techniques in single object tracking.
Finally, we suggest potential avenues for future research in single-object
tracking.
