Towards an Machine Learning-based Edge Computing Oriented Monitoring System for the Desert Border Surveillance Use Case
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60360DOI: 10.1109/ACCESS.2020.3042699
ISSN: 2169-3536
Publisher
IEEE
Date
2020Bibliographic citation
K. M. Bellazi, R. Marino, J. M. Lanza-Gutierrez and T. Riesgo, "Towards an Machine Learning-Based Edge Computing Oriented Monitoring System for the Desert Border Surveillance Use Case," in IEEE Access, vol. 8, pp. 218304-218322, 2020
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.1109/ACCESS.2020.3042699Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2020 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
The design of border surveillance systems is critical for most countries in the world, having
each border specific needs. This paper focuses on an Internet of Things oriented surveillance system to be
deployed in the Sahara Desert, which is composed of many unattended fixed platforms, where the nodes in
the edge have a Forward Looking InfraRed (FLIR) camera for field monitoring. To reduce communications
and decentralise the processing, IR images should be fully computed on the edge by an Automated Target
Recognition (ATR) algorithm, tracking and identifying targets of interest. As edge nodes are constrained
in energy and computing capacity, this work proposes two ATR systems to be executed in low-power
microprocessors. Both proposals are based on using Bag-of-Features for feature extraction and a supervised
algorithm for classification, both differing in segmenting the InfraRed image in regions of interest or working
directly with the whole image. Both proposals are successfully applied to infer about a dataset generated to
this end, getting a trade-off between computing cost and detection capacity. As a result, the authors obtained
a detection capacity of up to 97% and a frame rate of up to 5.71 and 59.17, running locally on the edge device
and the workstation, respectively.I
Files in this item
Files | Size | Format |
|
---|---|---|---|
Towards_an_IEEE_Access_2020.pdf | 3.210Mb |
|
Files | Size | Format |
|
---|---|---|---|
Towards_an_IEEE_Access_2020.pdf | 3.210Mb |
|
Collections
- CCOMPUT - Artículos [86]