  In high population cities, the gatherings of large crowds in public places
and public areas accelerate or jeopardize people safety and transportation,
which is a key challenge to the researchers. Although much research has been
carried out on crowd analytics, many of existing methods are problem-specific,
i.e., methods learned from a specific scene cannot be properly adopted to other
videos. Therefore, this presents weakness and the discovery of these
researches, since additional training samples have to be found from diverse
videos. This paper will investigate diverse scene crowd analytics with
traditional and deep learning models. We will also consider pros and cons of
these approaches. However, once general deep methods are investigated from
large datasets, they can be consider to investigate different crowd videos and
images. Therefore, it would be able to cope with the problem including to not
limited to crowd density estimation, crowd people counting, and crowd event
recognition. Deep learning models and approaches are required to have large
datasets for training and testing. Many datasets are collected taking into
account many different and various problems related to building crowd datasets,
including manual annotations and increasing diversity of videos and images. In
this paper, we will also propose many models of deep neural networks and
training approaches to learn the feature modeling for crowd analytics.
