Graph Embedded Convolutional Neural Networks in Human Crowd Detection for Drone Flight Safety

In this paper, we propose a novel human crowd detection method that uses deep convolutional neural networks for drone flight safety purposes. The first contribution of this paper is to provide lightweight architectures, as restricted by the computational capacity of the specific application, capable of effectively distinguishing between crowded and non-crowded scenes from drone-captured images, and provide crowd heatmaps which can be used to semantically enrich the flight maps by defining no-fly zones. The second contribution of this paper is to propose a novel generic regularization technique, based on the graph embedding framework, applicable to different deep architectures for generic classification problems. The experimental validation is performed on a new dataset constructed for the task of human crowd detection from drone-captured images, and indicates the effectiveness of the proposed detector, as well as of the proposed regularizers in terms of classification accuracy. Finally, since the proposed regularization scheme is applicable in generic classification problems, we have also conducted experiments on two additional datasets, where the enhanced performance of the regularizers is also validated.