  In spite of the many advantages of aerial imagery for crowd monitoring and
management at mass events, datasets of aerial images of crowds are still
lacking in the field. As a remedy, in this work we introduce a novel crowd
dataset, the DLR Aerial Crowd Dataset (DLR-ACD), which is composed of 33 large
aerial images acquired from 16 flight campaigns over mass events with 226,291
persons annotated. To the best of our knowledge, DLR-ACD is the first aerial
crowd dataset and will be released publicly. To tackle the problem of accurate
crowd counting and density map estimation in aerial images of crowds, this work
also proposes a new encoder-decoder convolutional neural network, the so-called
Multi-Resolution Crowd Network MRCNet. The encoder is based on the VGG-16
network and the decoder is composed of a set of bilinear upsampling and
convolutional layers. Using two losses, one at an earlier level and another at
the last level of the decoder, MRCNet estimates crowd counts and
high-resolution crowd density maps as two different but interrelated tasks. In
addition, MRCNet utilizes contextual and detailed local information by
combining high- and low-level features through a number of lateral connections
inspired by the Feature Pyramid Network (FPN) technique. We evaluated MRCNet on
the proposed DLR-ACD dataset as well as on the ShanghaiTech dataset, a
CCTV-based crowd counting benchmark. The results demonstrate that MRCNet
outperforms the state-of-the-art crowd counting methods in estimating the crowd
counts and density maps for both aerial and CCTV-based images.
