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 …

When overpopulated cities face frequent crowded events like strikes, demonstrations,
parades or other sorts of people gatherings, they are confronted to multiple security issues …

Recently, counting the number of people for crowd scenes is a hot topic because of its
widespread applications (eg video surveillance, public security). It is a difficult task in the …

We propose a network for Congested Scene Recognition called CSRNet to provide a data-
driven and deep learning method that can understand highly congested scenes and perform …

We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-
quality crowd density and count estimation by explicitly incorporating global and local …

Estimating crowd count in densely crowded scenes is an extremely challenging task due to
non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded …

We propose an attention-injective deformable convolutional network called ADCrowdNet for
crowd understanding that can address the accuracy degradation problem of highly …

In real-world crowd counting applications, the crowd densities vary greatly in spatial and
temporal domains. A detection based counting method will estimate crowds accurately in …

We propose a novel crowd counting approach that leverages abundantly available
unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped …

Deep convolutional networks (ConvNets) have achieved unprecedented performances on
many computer vision tasks. However, their adaptations to crowd counting on single images …