DroneNet: Crowd Density Estimation using Self-ONNs for Drones

TL;DR

DroneNet introduces a novel crowd density estimation model for drones using Self-ONNs, achieving higher accuracy with lower computational complexity compared to traditional CNN-based models.

Contribution

The paper presents DroneNet, a new crowd density estimation approach utilizing Self-ONNs, which enhances performance and efficiency over existing CNN-based methods.

Findings

DroneNet outperforms CNN-based models in accuracy.

Self-ONN provides lower computational complexity.

Effective on drone-view public datasets.

Abstract

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.