  Crowd counting from unconstrained scene images is a crucial task in many
real-world applications like urban surveillance and management, but it is
greatly challenged by the camera's perspective that causes huge appearance
variations in people's scales and rotations. Conventional methods address such
challenges by resorting to fixed multi-scale architectures that are often
unable to cover the largely varied scales while ignoring the rotation
variations. In this paper, we propose a unified neural network framework, named
Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues
in a learnable spatial transform module with a region-wise refinement process.
Specifically, our framework incorporates a Recurrent Spatial-Aware Refinement
(RSAR) module iteratively conducting two components: i) a Spatial Transformer
Network that dynamically locates an attentional region from the crowd density
map and transforms it to the suitable scale and rotation for optimal crowd
estimation; ii) a Local Refinement Network that refines the density map of the
attended region with residual learning. Extensive experiments on four
challenging benchmarks show the effectiveness of our approach. Specifically,
comparing with the existing best-performing methods, we achieve an improvement
of 12% on the largest dataset WorldExpo'10 and 22.8% on the most challenging
dataset UCF_CC_50.

Computer Vision and Pattern Recognition