Crowd Flow Detection from Drones with Fully Convolutional Networks and Clustering

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.
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