Video surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios. An interesting application of drone-based video surveillance is to estimate crowd densities (both pedestrians and vehicles) in public places. Deep learning using convolution neural networks (CNNs) is employed for automatic crowd counting and density estimation using images and videos. However, the performance and accuracy of such models typically depend upon the model architecture i.e., deeper CNN models improve accuracy at the cost of increased inference time. In this paper, we propose a novel crowd density estimation model for drones (DroneNet) using Self-organized Operational Neural Networks (Self-ONN). Self-ONN provides efficient learning capabilities with lower computational complexity as compared to CNN-based models. We tested our algorithm on two drone-view public datasets. Our evaluation shows that the proposed DroneNet shows superior performance on an equivalent CNN-based model.

翻译：使用无人机进行视频监控具有部署便捷、运动不受场景限制等优点，因而既方便又高效。无人机航拍视频监控的一个有趣应用是估算公共场所的人群密度（包括行人和车辆）。基于卷积神经网络（CNN）的深度学习被广泛用于利用图像和视频实现自动人群计数与密度估计。然而，此类模型的性能与精度通常取决于模型架构，即更深的CNN模型虽能提升精度，但会增加推理时间。本文提出一种针对无人机的创新人群密度估计模型（DroneNet），采用自组织操作神经网络（Self-ONN）。与基于CNN的模型相比，Self-ONN能以更低的计算复杂度提供高效的学习能力。我们在两个公开无人机视角数据集上测试了该算法。结果表明，所提出的DroneNet在性能上优于等效的基于CNN的模型。