Presentation + Paper
12 September 2021 Crowd change detection from VHR images acquired by UAV via deep features exploitation
Author Affiliations +
Abstract
Images acquired by RGB cameras on Unmanned Aerial Vehicles (UAVs) can be particularly useful to detect crowd in urban areas when restrictive conditions are imposed for the sake of public safety or health , such as during the Covid 19 pandemic. Together with acquired images, opportune pattern recognition techniques have to be considered to extract useful information. In this framework, features capturing the semantic rich information encompassed in VHR images have to be computed. In particular, Deep Neural Networks (DNN) have been recently proved to be able to extract useful features from data [1]. Moreover, in a transfer learning approach, a DNN pre-trained on a data set can be used to extract opportune features, named deep-features, from another data set, belonging to a different applicative domain [1], [2]. Here, a transfer learning technique is presented to produce change maps, detecting how people gathering increases, from VHR images. It is based on deep-features computed by using some pre-trained convolutional layers of AlexNet. The proposed methodology has been tested on a data set composed of several synthetic VHR images, that simulate crowd collecting in a park, as they can be acquired by RGB camera on a UAV flying at 10 meters height from the ground. The experimental results show that the proposed technique is able to efficiently detect change due to new people incoming in the scene or people that get away, with a low computational cost and in a near-real time operative mode.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Annarita D'Addabbo, Guido Pasquariello, Carmela Agnese De Donno, Angelo Amodio, Angelo Emanuele Fiorilla, and Andrea Palumbo "Crowd change detection from VHR images acquired by UAV via deep features exploitation", Proc. SPIE 11864, Remote Sensing Technologies and Applications in Urban Environments VI, 118640J (12 September 2021); https://doi.org/10.1117/12.2599915
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KEYWORDS
Unmanned aerial vehicles

RGB color model

Feature extraction

Convolutional neural networks

Image analysis

Neural networks

Safety

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