Counting Vehicles with Deep Learning in Onboard UAV Imagery

By Giuseppe Amato, Luca Ciampi, Fabrizio Falchi, Claudio Gennaro. 2019

47 citations recorded by the source corpus.

The integration of mobile and ubiquitous computing with deep learning methods is a promising emerging trend that aims at moving the processing task closer to the data source rather than bringing the data to a central node. The advantages of this approach range from bandwidth reduction, high scalability, to high reliability, just to name a few. In this paper, we propose a real-time deep learning approach to automatically detect and count vehicles in videos taken from a UAV (Unmanned Aerial Vehicle). Our solution relies on a convolutional neural network-based model fine-tuned to the specific domain of applications that is able to precisely localize instances of the vehicles using a regression approach, straight from image pixels to bounding box coordinates, reasoning globally about the image when making predictions and implicitly encoding contextual information. A comprehensive experimental evaluation on real-world datasets shows that our approach results in state-of-the-art performances. Furthermore, our solution achieves real-time performances by running at a speed of 4 Frames Per Second on an NVIDIA Jetson TX2 board, showing the potentiality of this approach for real-time processing in UAVs.

Subfields: Remote Sensing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Luca Ciampi
2. Carlos Santiago
3. Claudio Gennaro
4. João Paulo Costeira
5. Giuseppe Amato

Citations in the corpus

• Domain Adaptation for Traffic Density Estimation (2021, background): “Some relevant works present in the literature are (Ciampi et al., 2018), (Amato et al., 2019), (Amato et al., 2018), (Aich and Stavness, 2018), (Laradji et al., 2018).”
• Unsupervised vehicle counting via multiple camera domain adaptation (2020, methodology): “They fall in two main classes of methods: a) detection-based approaches ([2, 4, 1]) that try to identify and localize single instances of objects in the image and b)density-based techniques that rely on regression techniques to estimate a density map from the image, and where the”