Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling

Gatherings of thousands to millions of people occur frequently for an enormous variety of events, and automated counting of these high density crowds is used for safety, management, and measuring significance of these events. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (i$k$NN) maps, even when used directly in existing state-of-the-art network structures. We also provide a new network architecture MUD-i$k$NN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided i$k$NN labeling. This upsampling combined with the i$k$NN maps further outperforms the existing state-of-the-art methods. The full label comparison emphasizes the importance of the labeling scheme, with the i$k$NN labeling being particularly effective. We demonstrate the accuracy of our MUD-i$k$NN network and the i$k$NN labeling scheme on a variety of datasets.