  Deep learning models are known to function like the human brain. Due to their
functional mechanism, they are frequently utilized to accomplish tasks that
require human intelligence. Multi-target tracking (MTT) for video surveillance
is one of the important and challenging tasks, which has attracted the
researcher's attention due to its potential applications in various domains.
Multi-target tracking tasks require locating the objects individually in each
frame, which remains a huge challenge as there are immediate changes in
appearances and extreme occlusions of objects. In addition to that, the
Multitarget tracking framework requires multiple tasks to perform i.e. target
detection, estimating trajectory, associations between frame, and
re-identification. Various methods have been suggested, and some assumptions
are made to constrain the problem in the context of a particular problem. In
this paper, the state-of-the-art MTT models, which leverage from deep learning
representational power are reviewed.
