Crowd Counting from Unmanned Aerial Vehicles with Fully-Convolutional Neural Networks

Crowd analysis is receiving an increasing attention in the last years because of its social and public safety implications. One of the building blocks of crowd analysis is crowd counting and the associated crowd density estimation. Several commercially available drones are equipped with onboard cameras and embed powerful GPUs, making them an excellent platform for real-time crowd counting tools. This paper proposes a light-weight and fast fully-convolutional neural network to learn a regression model for crowd counting in images acquired from drones. A robust model is derived by training the network from scratch on a subset of the very challenging VisDrone dataset, which is characterized by a high variety of locations, environments, perspectives and lighting conditions. The derived model achieves an MAE of 8.86 and an RMSE of 15.07 on the test images, outperforming models developed by state-of-the-art light-weight architectures, that are MobileNetV2 and YOLOv3.