  Crowd counting on static images is a challenging problem due to scale
variations. Recently deep neural networks have been shown to be effective in
this task. However, existing neural-networks-based methods often use the
multi-column or multi-network model to extract the scale-relevant features,
which is more complicated for optimization and computation wasting. To this
end, we propose a novel multi-scale convolutional neural network (MSCNN) for
single image crowd counting. Based on the multi-scale blobs, the network is
able to generate scale-relevant features for higher crowd counting performances
in a single-column architecture, which is both accuracy and cost effective for
practical applications. Complemental results show that our method outperforms
the state-of-the-art methods on both accuracy and robustness with far less
number of parameters.
