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EnglishThe 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.
| Titolo: | Counting Vehicles with Deep Learning in Onboard UAV Imagery | |
| Autori interni: | CIAMPI, LUCA (Primo) | |
| Anno del prodotto: | 2019 | |
| Serie: | ||
| Handle: | http://hdl.handle.net/11568/1142538 | |
| ISBN: | 978-1-7281-2999-0 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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