Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used in a wide range of domains, from fast delivery, to video surveillance and aerial photography. However, as the use of drones increases, so does the need for public safety mechanisms. In particular, although current regulations generally forbid drones from flying over a crowd, unpredictable problems, such as adverse environmental conditions, could make the drone’s emergency landing unavoidable, even in the presence of a crowd.
At CILab we are developing novel crowd detection methods for drone safe landing, based on extremely light and fast fully-convolutional neural networks. The proposed architectures are based on two-loss models in which the main classification task, aimed at distinguishing between crowded and non-crowded scenes, is simultaneously assisted by regression tasks. In addition, the proposed method provides class activation heatmaps, useful for semantically augmenting the flight maps.
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Castellano, G., Castiello, C., Cianciotta, M., Mencar, C., Vessio, G., "Multi-view Convolutional Network for Crowd Counting in Drone-Captured Images", Computer Vision - ECCV 2020 Workshops, LNAI 12538, Springer, ISBN: 978-3-030-66822-8, 2020
Castellano, G., Castiello, C., Mencar, C., Vessio, G., "Crowd Counting from Unmanned Aerial Vehicles with Fully-Convolutional Neural Networks", 2020 International Joint Conference on Neural Networks (IJCNN 2020), within WCCI 2020, Glasgow, United Kingdom, 1-8, IEEE, ISBN: 978-1-7281-6926-2, 2020
Castellano, G., Castiello, C., Mencar, C., Vessio, G., "Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks", IEEE Access, 8, 64534-64544, IEEE, ISSN: 2169-3536, 2020 (open access)
Castellano, G., Castiello, C., Mencar, C., Vessio, G., "Crowd Detection for Drone Safe Landing Through Fully-Convolutional Neural Networks", SOFSEM 2020: Theory and Practice of Computer Science, LNCS 12011, 301-312, Springer, ISBN: 978-3-030-38918-5, 2020