  Bayesian nonparametric models offer a flexible and powerful framework for
statistical model selection, enabling the adaptation of model complexity to the
intricacies of diverse datasets. This survey intends to delve into the
significance of Bayesian nonparametrics, particularly in addressing complex
challenges across various domains such as statistics, computer science, and
electrical engineering. By elucidating the basic properties and theoretical
foundations of these nonparametric models, this survey aims to provide a
comprehensive understanding of Bayesian nonparametrics and their relevance in
addressing complex problems, particularly in the domain of multi-object
tracking. Through this exploration, we uncover the versatility and efficacy of
Bayesian nonparametric methodologies, paving the way for innovative solutions
to intricate challenges across diverse disciplines.
