论文标题
Dronenet：使用无人机的自我强调的人群密度估算
DroneNet: Crowd Density Estimation using Self-ONNs for Drones
论文作者
论文摘要
由于在许多情况下，使用无人机的视频监视既方便又有效。基于无人机的视频监视的一个有趣的应用是在公共场所估算人群密度（行人和车辆）。使用卷积神经网络（CNN）的深度学习用于使用图像和视频进行自动人群计数和密度估算。但是，这种模型的性能和准确性通常取决于模型体系结构，即更深的CNN模型以增加推理时间成本提高了准确性。在本文中，我们提出了一个新型的人群密度估计模型（DRONENET），使用自组织的操作神经网络（SelfNN）。与基于CNN的模型相比，自我关键是具有较低计算复杂性的有效学习能力。我们在两个无人机视图公共数据集上测试了算法。我们的评估表明，提出的Dronenet在基于同等的CNN模型上显示出卓越的性能。
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.
