  With multiple crowd gatherings of millions of people every year in events
ranging from pilgrimages to protests, concerts to marathons, and festivals to
funerals; visual crowd analysis is emerging as a new frontier in computer
vision. In particular, counting in highly dense crowds is a challenging problem
with far-reaching applicability in crowd safety and management, as well as
gauging political significance of protests and demonstrations. In this paper,
we propose a novel approach that simultaneously solves the problems of
counting, density map estimation and localization of people in a given dense
crowd image. Our formulation is based on an important observation that the
three problems are inherently related to each other making the loss function
for optimizing a deep CNN decomposable. Since localization requires
high-quality images and annotations, we introduce UCF-QNRF dataset that
overcomes the shortcomings of previous datasets, and contains 1.25 million
humans manually marked with dot annotations. Finally, we present evaluation
measures and comparison with recent deep CNN networks, including those
developed specifically for crowd counting. Our approach significantly
outperforms state-of-the-art on the new dataset, which is the most challenging
dataset with the largest number of crowd annotations in the most diverse set of
scenes.
