Multi-View Convolutional Network for Crowd Counting in Drone-Captured Images
Abstract
This paper proposes a novel lightweight and fast convolutional neural network to learn a regression model for crowd counting in images captured from drones. The learning system is initially based on a multi-input model trained on two different views of the same input for the task at hand: ( i ) real-world images; and ( ii ) corresponding synthetically created “crowd heatmaps”. The synthetic input is intended to help the network focus on the most important parts of the images. The network is trained from scratch on a subset of the VisDrone dataset. During inference, the synthetic path of the network is disregarded resulting in a traditional single-view model optimized for resource-constrained devices. The derived model achieves promising results on the test images, outperforming models developed by state-of-the-art lightweight architectures that can be used for crowd counting and detection.
Cite
Text
Castellano et al. "Multi-View Convolutional Network for Crowd Counting in Drone-Captured Images." European Conference on Computer Vision Workshops, 2020. doi:10.1007/978-3-030-66823-5_35Markdown
[Castellano et al. "Multi-View Convolutional Network for Crowd Counting in Drone-Captured Images." European Conference on Computer Vision Workshops, 2020.](https://mlanthology.org/eccvw/2020/castellano2020eccvw-multiview/) doi:10.1007/978-3-030-66823-5_35BibTeX
@inproceedings{castellano2020eccvw-multiview,
title = {{Multi-View Convolutional Network for Crowd Counting in Drone-Captured Images}},
author = {Castellano, Giovanna and Castiello, Ciro and Cianciotta, Marco and Mencar, Corrado and Vessio, Gennaro},
booktitle = {European Conference on Computer Vision Workshops},
year = {2020},
pages = {588-603},
doi = {10.1007/978-3-030-66823-5_35},
url = {https://mlanthology.org/eccvw/2020/castellano2020eccvw-multiview/}
}