Counting vehicles with deep learning in onboard UAV imagery

Amato G., Ciampi L., Falchi F., Gennaro C.

Onboard Embedded Processing  Real-Time Vehicle Detection  Convolutional Neural Networks  UAV  Object Counting  Deep Learning  Drones 

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.

Source: ISCC 2019 - IEEE Symposium on Computers and Communications, pp. 1–6, Barcelona , Spain, 30 June 2019 - 03 July 2019

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