Crowd counting, i.e., estimation number of the pedestrian in crowd images, is emerging as an important research problem
with the public security applications. A key component for the crowd counting systems is the construction of counting
models which are robust to various scenarios under facts such as camera perspective and physical barriers. In this paper,
we present an adaptive scenario discovery framework for crowd counting. The system is structured with two parallel
pathways that are trained with different sizes of the receptive field to represent different scales and crowd densities. After
ensuring that these components are present in the proper geometric configuration, a third branch is designed to adaptively
recalibrate the pathway-wise responses by discovering and modeling the dynamic scenarios implicitly. Our system is
able to represent highly variable crowd images and achieves state-of-the-art results in two challenging benchmarks.