Public Safety is nowadays a priority, cornerstone and major concern for governments, majors and policy makers in current (and future) smart cities. Notwithstanding the foregoing, large advances in ICT technologies are foretold to revolutionize our society and enhance our feeling of safety (and hopefully, wellbeing). This chapter presents an introduction to three of the most promising technological pillars considered to be spearheads in this transformation: Internet of things, understood as the data capillarity through billions of sensors, Intelligent Video Analytics and Data Mining Intelligence, the latter two enabling smarter contextual awareness and prediction of potential threats leading to proactive prevention of them. The associated horizontal economic implications of this evolution and its impact into the societal and economic fabric are also tackled. Part of the results and analysis produced in this chapter are the outcome of the work carried out in the FP7 EU project SafeCity, one of the eight Use Cases of the FI Programme. The Safety Transformation in the Future Internet Domain 191 to reduce emergency response time and urban crime: for example, digital surveillance cameras have been placed in many critical areas and buildings throughout cities and call dispatchers have been created to distribute the emergency calls. Moreover, advanced technological capabilities facilitate urban public safety systems to become not just more interconnected and efficient, but also smarter and self-adaptive. Instead of merely responding to crimes and emergencies after a critical situation, novel smart systems emerge to analyse, anticipate and, actually, contribute to preventing them before occurring. After the terrorist attacks of March 2004 in Madrid, the city developed a new fully integrated Emergency Response Centre which, after an incoming emergency call, simultaneously alerts the required emergency agency (police, ambulance and/or fire brigade). The system can recognize if alerts relate to a single or multiple incidents, and assign the right resources based on the requirements coming from the ground. Furthermore, specialized video analytics systems are successfully installed for traffic surveillance purposes. These are CCTV-based systems capable of automatically detect illegal vehicles behaviour (e.g. cars stopped in forbidden areas, going in the opposite direction), restricted entries behaviour (e.g. bike entering in a forbidden road), stolen vehicles, etc. In addition, M2M communications, that is, intelligent communications by enabled devices without human intervention, are nowadays present in home and industrial security monitoring systems and alarms. Several Public Safety organizations and Public Administrations are using sensor networks to monitor environmental conditions or to be temporally deployed driven by an emergency situation. Other advanced technologies are focused on enhancing emergency notification mechanisms, fire and enforcement records management, surveillance, etc. As presented, outstanding capabiliti...
The construction industry is on the path to digital transformation. One of the main challenges in this process is inspecting, assessing, and maintaining civil infrastructures and construction elements. However, Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) can support the tedious and time-consuming work inspection processes. This article presents an innovative object detection-based system which enables the detection and geo-referencing of different traffic signs from RGB images captured by a drone’s onboard camera, thus improving the realization of road element inventories in civil infrastructures. The computer vision component follows the typical methodology for a deep-learning-based SW: dataset creation, election and training of the most accurate object detection model, and testing. The result is the creation of a new dataset with a wider variety of traffic signs and an object detection-based system using Faster R-CNN to enable the detection and geo-location of traffic signs from drone-captured images. Despite some significant challenges, such as the lack of drone-captured images with labeled traffic signs and the imbalance in the number of images for traffic signal detection, the computer vision component allows for the accurate detection of traffic signs from UAV images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.