Crowd Flow Detection from Drones with Fully Convolutional Networks and Clustering
Giovanna Castellano, Corrado Mencar, Gaetano Sette, Francesco Saverio Troccoli, Gennaro Vessio · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
Crowd analysis from drones has attracted increasing attention in recent times, thanks to the ease of deployment and affordable cost of these devices. However, how this technology can provide a solution to crowd flow detection is still an explored research question. In this paper, we contribute by proposing a crowd flow detection method for video sequences shot by a drone. The method is mainly based on a Fully Convolutional Network model for crowd density estimation, which aims to provide a good compromise between effectiveness and efficiency, and clustering algorithms aimed at detecting the centroids of high-density areas in density maps. The method was tested on the VisDrone Crowd Counting dataset-characterized not by still images but by video sequences-providing promising results. This direction may open up new ways of analyzing high-level crowd behavior from drones.1