  State-of-the-art methods for counting people in crowded scenes rely on deep
networks to estimate crowd density. They typically use the same filters over
the whole image or over large image patches. Only then do they estimate local
scale to compensate for perspective distortion. This is typically achieved by
training an auxiliary classifier to select, for predefined image patches, the
best kernel size among a limited set of choices. As such, these methods are not
end-to-end trainable and restricted in the scope of context they can leverage.
  In this paper, we introduce an end-to-end trainable deep architecture that
combines features obtained using multiple receptive field sizes and learns the
importance of each such feature at each image location. In other words, our
approach adaptively encodes the scale of the contextual information required to
accurately predict crowd density. This yields an algorithm that outperforms
state-of-the-art crowd counting methods, especially when perspective effects
are strong.
