AI-based outdoor moving object detection for smart city surveillance

Yahia Said; Amjad A. Alsuwaylimi; Yahia Said; Amjad A. Alsuwaylimi

doi:10.3934/math.2024776

AIMS Mathematics

2024, Volume 9, Issue 6: 16015-16030. doi: 10.3934/math.2024776

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AI-based outdoor moving object detection for smart city surveillance

Yahia Said ^{1
,
,},
Amjad A. Alsuwaylimi ²

1.
Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia
2.
Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia

Received: 06 March 2024 Revised: 18 April 2024 Accepted: 28 April 2024 Published: 07 May 2024
MSC : 68T07, 68T45

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.
- moving objects detection,
- smart cities,
- deep learning,
- artificial intelligence
Citation: Yahia Said, Amjad A. Alsuwaylimi. AI-based outdoor moving object detection for smart city surveillance[J]. AIMS Mathematics, 2024, 9(6): 16015-16030. doi: 10.3934/math.2024776

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Abstract

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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