A Visual Sensor Network for Parking Lot Occupancy Detection in Smart Cities

Technology is quickly revolutionizing our everyday lives, helping us to perform complex tasks. The Internet of Things (IoT) paradigm is getting more and more popular and is key to the development of Smart Cities. Among all the applications of IoT in the context of Smart Cities, real-time parking lot occupancy detection recently gained a lot of attention. Solutions based on computer vision yield good performance in terms of accuracy and are deployable on top of visual sensor networks. Since the problem of detecting vacant parking lots is usually distributed over multiple cameras, adhoc algorithms for content acquisition and transmission are to be devised. A traditional paradigm consists in acquiring and encoding images or videos and transmitting them to a central controller, which is responsible for analyzing such content. A novel paradigm, which moves part of the analysis to sensing devices, is quickly becoming popular. We propose a system for distributed parking lot occupancy detection based on the latter paradigm, showing that onboard analysis and transmission of simple features yield better performance with respect to the traditional paradigm in terms of the overall rate-energy-accuracy performance

Review on Automatic Car Parking Indicator System

Parking is costly and limited in almost every major city in the world. An Automatic car parking systems for meeting near term parking demand are needed. There is need to develop a vacant parking slot detection and tracking system. Around view monitor (AVM) image sequence makes it possible with 360-degree scene Bird’s eye view camera. Around view monitor (AVM) captures the image sequence and on combining of each images empty slot is detected. The Ultrasonic sensor is useful to determine the adjacent vehicle. Hierarchical tree structure based parking slot marking method is used to recognize the parking slot marking. After combining sequentially detected parking slot, empty parking slot is recognized and the driver has to select one of the empty parking slots and drive into it. DOI: 10.17762/ijritcc2321-8169.16048

Algorithms for Image Analysis in Traffic Surveillance Systems

Import 23/07/2015The presence of various surveillance systems in many areas of the modern society is indisputable and the most perceptible are the video surveillance systems. This thesis mainly describes novel algorithm for vision-based estimation of the parking lot occupancy and the closely related topics of pre-processing of images captured under harsh conditions. The developed algorithms have their practical application in the parking guidance systems which are still more popular. One part of this work also tries to contribute to the specific area of computer graphics denoted as direct volume rendering (DVR).Přítomnost nejrůznějších dohledových systémů v mnoha oblastech soudobé společnosti je nesporná a systémy pro monitorování dopravy jsou těmi nejviditelnějšími. Hlavní část této práce se věnuje popisu nového algoritmu pro detekci obsazenosti parkovacích míst pomocí analýzy obrazu získaného z kamerového systému. Práce se také zabývá tématy úzce souvisejícími s předzpracováním obrazu získaného za ztížených podmínek. Vyvinuté algoritmy mají své praktické uplatnění zejména v oblasti pomocných parkovacích systémů, které se stávají čím dál tím více populárními. Jedna část této práce se snaží přispět do oblasti počítačové grafiky označované jako přímá vizualizace objemových dat.Prezenční460 - Katedra informatikyvyhově

Analyse et gestion de l’occupation de places de stationnement par vision artificielle

Cet article présente un système de surveillance basé sur la vision pour le développement de services de gestion de places de parking. Le système présenté est un système adaptable pour l'analyse de places de stationnement dans des parkings de différentes configurations. Dans ce but, des expérimentations ont été menées sous différentes prises de vue en utilisant une caméra connectée à une station de travail mobile. Les résultats obtenus montrent la faisabilité du système dans l'analyse et dans la gestion des emplacements de parking avec des véhicules

A Dynamic Information-Based Parking Guidance for Megacities considering Both Public and Private Parking

The constantly increasing number of cars in the megacities is causing severe parking problems. To resolve this problem, many cities adopt parking guidance system as a part of intelligent transportation system (ITS). However, the current parking guidance system stays in its infant stage since the obtainable information is limited. To enhance parking management in the megacity and to provide better parking guidance to drivers, this study introduces an intelligent parking guidance system and proposes a new methodology to operate it. The introduced system considers both public parking and private parking so that it is designed to maximize the use of spatial resources of the city. The proposed methodology is based on the dynamic information related parking in the city and suggests the best parking space to each driver. To do this, two kinds of utility functions which assess parking spaces are developed. Using the proposed methodology, different types of parking management policies are tested through the simulation. According to the experimental test, it is shown that the centrally managed parking guidance can give better results than individually preferred parking guidance. The simulation test proves that both a driver???s benefits and parking management of a city from various points of view can be improved by using the proposed methodology

Combinação de características texturais para a classificação automática de vagas de estacionamento

Resumo: A crescente frota de veículos nas cidades aliada à falta de planejamento urbano têm tornado cada vez mais difícil a tarefa de se encontrar vagas de estacionamento, forçando os motoristas a procurarem alternativas como os pátios de estacionamentos públicos ou privados, que são dedicados exclusivamente a acomodar um grande número de veículos. Uma metodologia viável para a gerência das vagas individuais desses estabelecimentos, ou das vagas laterais presentes em vias públicas, apesar de ser um grande desafio, pode prover informações importantes para a engenharia de tráfego, como o índice de ocupação das vagas, as áreas mais utilizadas e a detecção de superlotações, além de poder servir como base para sistemas que executem tarefas como a detecção de veículos estacionados irregularmente ou que guiem o motorista até a vaga livre mais próxima. Neste trabalho é proposta uma metodologia para classificação de vagas de estacionamento baseada em imagens coletadas de câmeras fixas, capaz de lidar com as complexidades impostas a sistemas que capturam imagens em ambientes abertos, como as variações de luminosidade, a presença de sombras e os ruídos causados pelas variações climáticas. Para cumprir essa tarefa são utilizados descritores de textura baseados nos Padrões Locais Binários e na Quantização de Fase Local, que têm mostrado excelentes resultados em diversos trabalhos. Para possibilitar os testes deste e de outros trabalhos relacionados à detecção de veículos em áreas de estacionamentos, foi criada uma base de imagens que atualmente contém cerca de 1.300.000 amostras de vagas individuais coletadas de dois estacionamentos distintos, de diferentes ângulos e em diversas condições climáticas, a qual está disponível para trabalhos de pesquisa. Os resultados dos experimentos mostram que os classificadores treinados com os descritores de textura são capazes de atingir excelentes taxas de acertos, próximas aos 100%. Testes com amostras coletadas de estacionamentos ou de ângulos que não participaram dos treinamentos dos classificadores também foram executados, e mostraram que os classificadores ainda são capazes de atingir boas taxas de acertos, geralmente próximas aos 85%. Métodos de combinação de classificadores também foram utilizados e se mostraram capazes de melhorar os desempenhos dos classificadores, principalmente nos testes com amostras coletadas de estacionamentos ou ângulos diferentes dos utilizados nos treinamentos dos classificadores

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed