# DroneNet: Crowd Density Estimation using Self-ONNs for Drones

arXiv: https://arxiv.org/abs/2211.07137 · HTML: https://mlsanity.com/paper/2211.07137/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Bayesian Loss for Crowd Count Estimation with Point Supervision](https://mlsanity.com/paper/1908.03684/) (2019)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning](https://mlsanity.com/paper/2003.02437/) (2020)
- [Context-Aware Crowd Counting](https://mlsanity.com/paper/1811.10452/) (2018)
- [Switching Convolutional Neural Network for Crowd Counting](https://mlsanity.com/paper/1708.00199/) (2017)
- [Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network](https://mlsanity.com/paper/1903.00853/) (2019)
- [CrowdNet: A Deep Convolutional Network for Dense Crowd Counting](https://mlsanity.com/paper/1608.06197/) (2016)
- [CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting](https://mlsanity.com/paper/1707.09605/) (2017)
- [Operational Neural Networks](https://mlsanity.com/paper/1902.11106/) (2019)
- [Self-Organized Operational Neural Networks for Severe Image Restoration Problems](https://mlsanity.com/paper/2008.12894/) (2020)
- [Multi-scale Convolutional Neural Networks for Crowd Counting](https://mlsanity.com/paper/1702.02359/) (2017)
- [Early Detection of Myocardial Infarction in Low-Quality Echocardiography](https://mlsanity.com/paper/2010.02281/) (2020)
- [Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives](https://mlsanity.com/paper/2209.07271/) (2022)
- [TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting](https://mlsanity.com/paper/2202.08517/) (2022)
- [Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images](https://mlsanity.com/paper/1805.11123/) (2018)
- [A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting](https://mlsanity.com/paper/2202.03843/) (2022)
- [Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography](https://mlsanity.com/paper/2103.14734/) (2021)

## Built on by (most cited first, 3 of 3)

- [Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs](https://mlsanity.com/paper/2310.07245/) (2023)
- [Visual Crowd Analysis: Open Research Problems](https://mlsanity.com/paper/2308.10677/) (2023)
- [Accelerating Deep Learning with Fixed Time Budget](https://mlsanity.com/paper/2410.03790/) (2024)

## Abstract (arXiv)

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.