I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making
Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narrow down to certain frames among thousands of video clips to a specific incident. A real-time, proactive surveillance system is desirable, which could i...
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Online Access: | https://dx.doi.org/10.48550/arxiv.1909.05776 https://arxiv.org/abs/1909.05776 |
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ftdatacite:10.48550/arxiv.1909.05776 2023-05-15T16:02:07+02:00 I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making Nikouei, Seyed Yahya Chen, Yu Aved, Alexander Blasch, Erik Faughnan, Timothy R. 2019 https://dx.doi.org/10.48550/arxiv.1909.05776 https://arxiv.org/abs/1909.05776 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Article CreativeWork article Preprint 2019 ftdatacite https://doi.org/10.48550/arxiv.1909.05776 2022-03-10T16:29:05Z Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narrow down to certain frames among thousands of video clips to a specific incident. A real-time, proactive surveillance system is desirable, which could instantly detect dubious personnel, identify suspicious activities, or raise momentous alerts. The recent proliferation of the edge computing paradigm allows more data-intensive tasks to be accomplished by smart edge devices with lightweight but powerful algorithms. This paper presents a forensic surveillance strategy by introducing an Instant Suspicious Activity identiFication at the Edge (I-SAFE) using fuzzy decision making. A fuzzy control system is proposed to mimic the decision-making process of a security officer. Decisions are made based on video features extracted by a lightweight Deep Machine Learning (DML) model. Based on the requirements from the first-line law enforcement officers, several features are selected and fuzzified to cope with the state of uncertainty that exists in the officers' decision-making process. Using features in the edge hierarchy minimizes the communication delay such that instant alerting is achieved. Additionally, leveraging the Microservices architecture, the I-SAFE scheme possesses good scalability given the increasing complexities at the network edge. Implemented as an edge-based application and tested using exemplary and various labeled dataset surveillance videos, the I-SAFE scheme raises alerts by identifying the suspicious activity in an average of 0.002 seconds. Compared to four other state-of-the-art methods over two other data sets, the experimental study verified the superiority of the I-SAFE decentralized method. : Manuscript has been accepted and to be presented at the Fourth ACM/IEEE Symposium on Edge Computing, Washington DC, November 7-9, 2019 Article in Journal/Newspaper DML DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
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Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Nikouei, Seyed Yahya Chen, Yu Aved, Alexander Blasch, Erik Faughnan, Timothy R. I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making |
topic_facet |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
description |
Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narrow down to certain frames among thousands of video clips to a specific incident. A real-time, proactive surveillance system is desirable, which could instantly detect dubious personnel, identify suspicious activities, or raise momentous alerts. The recent proliferation of the edge computing paradigm allows more data-intensive tasks to be accomplished by smart edge devices with lightweight but powerful algorithms. This paper presents a forensic surveillance strategy by introducing an Instant Suspicious Activity identiFication at the Edge (I-SAFE) using fuzzy decision making. A fuzzy control system is proposed to mimic the decision-making process of a security officer. Decisions are made based on video features extracted by a lightweight Deep Machine Learning (DML) model. Based on the requirements from the first-line law enforcement officers, several features are selected and fuzzified to cope with the state of uncertainty that exists in the officers' decision-making process. Using features in the edge hierarchy minimizes the communication delay such that instant alerting is achieved. Additionally, leveraging the Microservices architecture, the I-SAFE scheme possesses good scalability given the increasing complexities at the network edge. Implemented as an edge-based application and tested using exemplary and various labeled dataset surveillance videos, the I-SAFE scheme raises alerts by identifying the suspicious activity in an average of 0.002 seconds. Compared to four other state-of-the-art methods over two other data sets, the experimental study verified the superiority of the I-SAFE decentralized method. : Manuscript has been accepted and to be presented at the Fourth ACM/IEEE Symposium on Edge Computing, Washington DC, November 7-9, 2019 |
format |
Article in Journal/Newspaper |
author |
Nikouei, Seyed Yahya Chen, Yu Aved, Alexander Blasch, Erik Faughnan, Timothy R. |
author_facet |
Nikouei, Seyed Yahya Chen, Yu Aved, Alexander Blasch, Erik Faughnan, Timothy R. |
author_sort |
Nikouei, Seyed Yahya |
title |
I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making |
title_short |
I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making |
title_full |
I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making |
title_fullStr |
I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making |
title_full_unstemmed |
I-SAFE: Instant Suspicious Activity identiFication at the Edge using Fuzzy Decision Making |
title_sort |
i-safe: instant suspicious activity identification at the edge using fuzzy decision making |
publisher |
arXiv |
publishDate |
2019 |
url |
https://dx.doi.org/10.48550/arxiv.1909.05776 https://arxiv.org/abs/1909.05776 |
genre |
DML |
genre_facet |
DML |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1909.05776 |
_version_ |
1766397721795624960 |