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

MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagery