Machine vision based smart parking system using Internet of Things

It is expected that in the next decade, majority of world population will be living in cities. Better public services and infrastructures in the city are needed to cope with the booming population. City vehicles that cruising for parking have indirectly causing traffic, making one harder to travel around the city. Thus, a smart parking system can certainly lays the foundation to build a smart city. This paper proposed a cost-effective IoT smart parking system to monitor city parking space and provide real-time parking information to drivers. Moreover, instead of the conventional approach that uses embedded sensors to detect vehicles in the parking area, camera image and machine vision technology are used to obtain the parking status. In the prototype, twenty outdoor parking lots are covered using a 5 megapixel camera connected to Raspberry Pi 3 installed at the 5th floor of the nearby building. Machine vision in this project that involved motion tracking and Canny edge detection are programmed in Python 2 using OpenCV technology. Corresponding data is uploaded to an IoT platform called Ubidots for possible monitoring activity. An Android mobile application is designed for user to download real-time data of parking information. This paper introduces a low cost smart parking system with the overall detection accuracy of 96.40%. Also, the mobile application allows users to alert other car owners for any emergency incidents and double parking blockage. The developed system can provide a platform for users to search for empty car parking with ease and reduce the traffic issues such as illegal double parking especially in the urban area

Annual Report Fiscal Year July 1, 2009-June 30, 2010

https://digitalcommons.memphis.edu/govpubs-tn-miscellaneous-departmental-publications-department-children-services/1011/thumbnail.jp

Trailgazers: A Scoping Study of Footfall Sensors to Aid Tourist Trail Management in Ireland and Other Atlantic Areas of Europe

This paper examines the current state of the art of commercially available outdoor footfall sensor technologies and defines individually tailored solutions for the walking trails involved in an ongoing research project. Effective implementation of footfall sensors can facilitate quantitative analysis of user patterns, inform maintenance schedules and assist in achieving management objectives, such as identifying future user trends like cyclo-tourism. This paper is informed by primary research conducted for the EU funded project TrailGazersBid (hereafter referred to as TrailGazers), led by Donegal County Council, and has Sligo County Council and Causeway Coast and Glens Council (NI) among the 10 project partners. The project involves three trails in Ireland and five other trails from Europe for comparison. It incorporates the footfall capture and management experiences of trail management within the EU Atlantic area and desk-based research on current footfall technologies and data capture strategies. We have examined 6 individual types of sensor and discuss the advantages and disadvantages of each. We provide key learnings and insights that can help to inform trail managers on sensor options, along with a decision-making tool based on the key factors of the power source and mounting method. The research findings can also be applied to other outdoor footfall monitoring scenarios

Efficient Deep Learning Models for Privacy-preserving People Counting on Low-resolution Infrared Arrays

Ultra-low-resolution Infrared (IR) array sensors offer a low-cost, energy-efficient, and privacy-preserving solution for people counting, with applications such as occupancy monitoring. Previous work has shown that Deep Learning (DL) can yield superior performance on this task. However, the literature was missing an extensive comparative analysis of various efficient DL architectures for IR array-based people counting, that considers not only their accuracy, but also the cost of deploying them on memory- and energy-constrained Internet of Things (IoT) edge nodes. In this work, we address this need by comparing 6 different DL architectures on a novel dataset composed of IR images collected from a commercial 8x8 array, which we made openly available. With a wide architectural exploration of each model type, we obtain a rich set of Pareto-optimal solutions, spanning cross-validated balanced accuracy scores in the 55.70-82.70% range. When deployed on a commercial Microcontroller (MCU) by STMicroelectronics, the STM32L4A6ZG, these models occupy 0.41-9.28kB of memory, and require 1.10-7.74ms per inference, while consuming 17.18-120.43 $\mu$J of energy. Our models are significantly more accurate than a previous deterministic method (up to +39.9%), while being up to 3.53x faster and more energy efficient. Further, our models' accuracy is comparable to state-of-the-art DL solutions on similar resolution sensors, despite a much lower complexity. All our models enable continuous, real-time inference on a MCU-based IoT node, with years of autonomous operation without battery recharging.Comment: This article has been accepted for publication in IEEE Internet of Things Journa

Intelligent Transportation in Acadia National Park

This project determined the feasibility of implementing an intelligent transportation system (ITS) in Acadia National Park. The features of an ITS were researched and discussed. The components of Acadia’s previous ITS were evaluated. New ITS technologies for Acadia were investigated. Three sensor types were identified to count cars: magnetometers, induction loops, and cameras. Three traveler information systems were identified: dynamic message signs, websites, and mobile applications. The logistics of implementing these systems were assessed, and a cost analysis performed. The team recommended magnetometer sensors with a dynamic message sign to monitor the Sand Beach parking lot and a centralized travel website. The system can be expanded to other popular locations in the future

Detection and identifitication of registration and fishing gear in vessels

Illegal, unreported and unregulated (IUU) fishing is a global menace to both marine ecosystems and sustainable fisheries. IUU products often come from fisheries lacking conservation and management measures, which allows the violation of bycatch limits or unreported catching. To counteract such issue, some countries adopted vessel monitoring systems (VMS) in order to track and monitor the activities of fishing vessels. The VMS approach is not flawless and as such, there are still known cases of IUU fishing. The present work is integrated in a project PT2020 SeeItAll of the company Xsealence and was included in INOV tasks in which a monitoring system using video cameras in the Ports (Non-boarded System) was developed, in order to detect registrations of vessels. This system registers the time of entry or exit of the vessel in the port. A second system (Boarded System) works with a camera placed in each vessel and an automatic learning algorithm detects and records fishing activities, for a comparison with the vessel’s fishing report.A pesca ilegal, não declarada e não regulamentada (INDNR) é uma ameaça global tanto para os ecossistemas marinhos quanto para a pesca sustentável. Os produtos INDNR são frequentemente provenientes de pescas que não possuem medidas de conservação e de gestão, o que permite a violação dos limites das capturas ou a captura não declarada. Para contrariar esse problema, alguns países adotaram sistemas de monitoramento de embarcações (VMS) para acompanhar e monitorar as atividades dos navios de pesca. A abordagem VMS não é perfeita e, como tal, ainda há casos conhecidos de pesca INDNR. O presente trabalho encontra-se integrado num projeto PT2020 SeeItAll da empresa Xsealence. Este trabalho integrado nas tarefas do INOV no qual foi desenvolvido um sistema de monitorização das entradas dos navios nos Portos (Sistema não embarcado) no qual pretende-se desenvolver um sistema que detete as matriculas dos navios registando a hora de entrada e saída do porto com recurso da camaras de vídeo. A outra componente (sistema embarcado) é colocada em cada embarcação uma camara de video e, recorrendo a aprendizagem automática e um sistema de CCTV, são detetadas as atividades de pesca e gravadas, para posterior comparação com o relatório de pesca do navio