Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs

@article{Sindagi2017GeneratingHC,
  title={Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs},
  author={Vishwanath A. Sindagi and Vishal M. Patel},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={1879-1888}
}
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. [] Key Method 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…

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