Smart video surveillance system based on edge computing
Authors
Cob Parro, Antonio Carlos; Losada Gutiérrez, Cristina; Marrón Romera, Marta; Gardel Vicente, Alfredo; Bravo Muñoz, IgnacioIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/60084DOI: 10.3390/s21092958
ISSN: 1424-8220
Publisher
MDPI
Date
2021-04-23Funders
European Commission
Ministerio de Economía y Competitividad
Bibliographic citation
Cob-Parro, A.C.; Losada-Gutiérrez, C.; Marrón-Romera, M.; Gardel-Vicente, A.; Bravo-Muñoz, I. Smart Video Surveillance System Based on Edge Computing. Sensors 2021, 21, 2958.
Keywords
Machine learning
Embedded systems
Video-surveillance
Mobilenet-SSD
Vision processor unit
Edge node
Artificial intelligence
Project
info:eu-repo/grantAgreement/EC/H2020/814962/EU/A holistic passenger ship evacuation and rescue ecosystem/PALAEMON
info:eu-repo/grantAgreement/MINECO//TIN2016-75982-C2-1-R/ES/DETECCION SEMANTICA MULTISENSORIAL DE SITUACIONES ANOMALAS EN ENTORNOS SIN RESTRICCIONES/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/s21092958Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2021 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people?s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system. ;
Files in this item
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Smart_Cob_Sensors_2021.pdf | 1.660Mb |
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Smart_Cob_Sensors_2021.pdf | 1.660Mb |
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