Density-based clustering with fully-convolutional networks for crowd flow detection from drones
This addresses crowd analysis for drone-based surveillance, though it appears incremental as it adapts existing methods to video sequences.
The paper tackled crowd flow detection from drone videos by proposing a fully-convolutional network for clustering and tracking crowd-dense areas, achieving effective and efficient results on the VisDrone Crowd Counting datasets.
Crowd analysis from drones has attracted increasing attention in recent times due to the ease of use and affordable cost of these devices. However, how this technology can provide a solution to crowd flow detection is still an unexplored research question. To this end, we propose a crowd flow detection method for video sequences shot by a drone. The method is based on a fully-convolutional network that learns to perform crowd clustering in order to detect the centroids of crowd-dense areas and track their movement in consecutive frames. The proposed method proved effective and efficient when tested on the Crowd Counting datasets of the VisDrone challenge, characterized by video sequences rather than still images. The encouraging results show that the proposed method could open up new ways of analyzing high-level crowd behavior from drones.