  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 contextual information of crowd images. The
proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local
Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN).
GCE is a VGG-16 based CNN that encodes global context and it is trained to
classify input images into different density classes, whereas LCE is another
CNN that encodes local context information and it is trained to perform
patch-wise classification of input images into different density classes. DME
is a multi-column architecture-based CNN that aims to generate high-dimensional
feature maps from the input image which are fused with the contextual
information estimated by GCE and LCE using F-CNN. To generate high resolution
and high-quality density maps, F-CNN uses a set of convolutional and
fractionally-strided convolutional layers and it is trained along with the DME
in an end-to-end fashion using a combination of adversarial loss and
pixel-level Euclidean loss. Extensive experiments on highly challenging
datasets show that the proposed method achieves significant improvements over
the state-of-the-art methods.
