AI-based outdoor moving object detection for smart city surveillance
In: AIMS Mathematics, Jg. 9 (2024), Heft 6, S. 16015-16030
Online
academicJournal
Zugriff:
One essential component of the futuristic way of living in "smart cities" is the installation of surveillance cameras. There are a wide variety of applications for surveillance cameras, including but not limited to: investigating and preventing crimes, identifying sick individuals (coronavirus), locating missing persons, and many more. In this research, we provided a system for smart city outdoor item recognition using visual data collected by security cameras. The object identification model used by the proposed outdoor system was an enhanced version of RetinaNet. A state of the art object identification model, RetinaNet boasts lightning-fast processing and pinpoint accuracy. Its primary purpose was to rectify the focal loss-based training dataset's inherent class imbalance. To make the RetinaNet better at identifying tiny objects, we increased its receptive field with custom-made convolution blocks. In addition, we adjusted the number of anchors by decreasing their scale and increasing their ratio. Using a mix of open-source datasets including BDD100K, MS COCO, and Pascal Vocab, the suggested outdoor object identification system was trained and tested. While maintaining real-time operation, the suggested system's performance has been markedly enhanced in terms of accuracy.
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AI-based outdoor moving object detection for smart city surveillance
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Autor/in / Beteiligte Person: | Said, Yahia ; Alsuwaylimi, Amjad A. |
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Zeitschrift: | AIMS Mathematics, Jg. 9 (2024), Heft 6, S. 16015-16030 |
Veröffentlichung: | AIMS Press, 2024 |
Medientyp: | academicJournal |
ISSN: | 2473-6988 (print) ; 4664-9840 (print) |
DOI: | 10.3934/math.2024776?viewType=HTML |
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