Smart City Gnosys
Smart city article details
| Title | Ai-Based Outdoor Moving Object Detection For Smart City Surveillance |
|---|---|
| ID_Doc | 6997 |
| Authors | Said Y.; Alsuwaylimi A.A. |
| Year | 2024 |
| Published | AIMS Mathematics, 9, 6 |
| DOI | http://dx.doi.org/10.3934/math.2024776 |
| 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. © 2024 the Author(s), licensee AIMS Press. |
| Author Keywords | artificial intelligence; deep learning; moving objects detection; smart cities |
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