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Embedded Vision Intelligence for the Safety of Smart Cities. | LitMetric

AI Article Synopsis

  • - Advances in AI and embedded systems have enabled automated video surveillance in smart cities, reducing the need for human oversight and increasing data availability for safety purposes.
  • - Improvements in hardware and lightweight software, like EdgeX Foundry, allow for sophisticated deep learning algorithms to be implemented on resource-constrained devices, enhancing data collection and processing at the edge.
  • - The development of two Edge Smart Camera Systems, including custom hardware and a Video Analytics Edge Computing (VAEC) system for person detection, shows promise for improving situational awareness in smart city environments, as demonstrated by extensive experimental validation.

Article Abstract

Advances in Artificial intelligence (AI) and embedded systems have resulted on a recent increase in use of image processing applications for smart cities' safety. This enables a cost-adequate scale of automated video surveillance, increasing the data available and releasing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Additionally, new lightweight open-source middleware for constrained resource devices, such as EdgeX Foundry, have appeared to facilitate the collection and processing of data at sensor level, with communication capabilities to exchange data with a cloud enterprise application. The objective of this work is to show and describe the development of two Edge Smart Camera Systems for safety of Smart cities within S4AllCities H2020 project. Hence, the work presents hardware and software modules developed within the project, including a custom hardware platform specifically developed for the deployment of deep learning models based on the I.MX8 Plus from NXP, which considerably reduces processing and inference times; a custom Video Analytics Edge Computing (VAEC) system deployed on a commercial NVIDIA Jetson TX2 platform, which provides high level results on person detection processes; and an edge computing framework for the management of those two edge devices, namely Distributed Edge Computing framework, DECIoT. To verify the utility and functionality of the systems, extended experiments were performed. The results highlight their potential to provide enhanced situational awareness and demonstrate the suitability for edge machine vision applications for safety in smart cities.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783517PMC
http://dx.doi.org/10.3390/jimaging8120326DOI Listing

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