  Estimating count and density maps from crowd images has a wide range of
applications such as video surveillance, traffic monitoring, public safety and
urban planning. In addition, techniques developed for crowd counting can be
applied to related tasks in other fields of study such as cell microscopy,
vehicle counting and environmental survey. The task of crowd counting and
density map estimation is riddled with many challenges such as occlusions,
non-uniform density, intra-scene and inter-scene variations in scale and
perspective. Nevertheless, over the last few years, crowd count analysis has
evolved from earlier methods that are often limited to small variations in
crowd density and scales to the current state-of-the-art methods that have
developed the ability to perform successfully on a wide range of scenarios. The
success of crowd counting methods in the recent years can be largely attributed
to deep learning and publications of challenging datasets. In this paper, we
provide a comprehensive survey of recent Convolutional Neural Network (CNN)
based approaches that have demonstrated significant improvements over earlier
methods that rely largely on hand-crafted representations. First, we briefly
review the pioneering methods that use hand-crafted representations and then we
delve in detail into the deep learning-based approaches and recently published
datasets. Furthermore, we discuss the merits and drawbacks of existing
CNN-based approaches and identify promising avenues of research in this rapidly
evolving field.
