AI-based outdoor moving object detection for smart city surveillance

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.

Keywords

• moving objects detection
• smart cities
• deep learning
• artificial intelligence