  Neuroscience has traditionally relied on manually observing lab animals in
controlled environments. Researchers usually record animals behaving in free or
restrained manner and then annotate the data manually. The manual annotation is
not desirable for three reasons; one, it is time consuming, two, it is prone to
human errors and three, no two human annotators will 100\% agree on annotation,
so it is not reproducible. Consequently, automated annotation of such data has
gained traction because it is efficient and replicable. Usually, the automatic
annotation of neuroscience data relies on computer vision and machine leaning
techniques. In this article, we have covered most of the approaches taken by
researchers for locomotion and gesture tracking of lab animals. We have divided
these papers in categories based upon the hardware they use and the software
approach they take. We also have summarized their strengths and weaknesses.
