Deep Learning Based Parking Vacancy Detection for Smart Cities

Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission. We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services

Vacancy state detector oriented to convolutional neural network, background subtraction and embedded systems

Dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáMuch has been discussed recently related to population ascension, the reasons for this event, and, in particular, the aspects of society affected. Over the years, the city governments realized a higher level of growth, mainly in terms of urban scale, technology, and individuals numbers. It comprises improvements and investments in their structure and policies, motivated by improving conditions in population live quality and reduce environmental, energy, fuel, time, and money resources, besides population living costs, including the increasing demand for parking structures accessible to the general or private-public, and a waste of substantial daily time and fuel, disturbing the population routinely. Therefore, one way to achieve that challenge is focused on reducing energy, money, and time costs to travel to work or travel to another substantial location. That work presents a robust, and low computational power Smart Parking system adaptive to several environments changes to detect and report vacancy states in a parking space oriented to Deep Learning, and Embedded Systems. This project consists of determining the parking vacancy status through statistical and image processing methods, creates a robust image data set, and the Convolutional Neural Network model focused on predict three final classes. In order to save computational power, this approach uses the Background Subtraction based on the Mixture of Gaussian method, only updating parking space status, in which large levels of motion are detected. The proposed model presents 94 percent of precision at the designed domain.Muito se discutiu recentemente sobre a ascensão populacional, as razões deste evento e, em particular, os aspectos da sociedade afetados. Ao longo dos anos, os governos perceberam um grande nível de crescimento, principalmente em termos de escala urbana, tecnologia e número de indivíduos. Este fato deve-se a melhorias e investimentos na estrutura urbana e políticas motivados por melhorar as condições de qualidade de vida da população e reduzir a utilização de recursos ambientais, energéticos, combustíveis, temporais e monetários, além dos custos de vida da população, incluindo a crescente demanda por estruturas de estacionamento acessíveis ao público em geral ou público-privado. Portanto, uma maneira de alcançar esse desafio é manter a atenção na redução de custos de energia, dinheiro e tempo para viajar para o trabalho ou para outro local substancial. Esse trabalho apresenta um sistema robusto de Smart Parking, com baixo consumo computacional, adaptável a diversas mudanças no ambiente observado para detectar e relatar os estados das vagas de estacionamento, orientado por Deep Learning e Embedded Systems. Este projeto consiste em determinar o status da vaga de estacionamento por meio de métodos estatísticos e de processamento de imagem, criando um conjunto robusto de dados e um modelo de Rede Neuronal Convolucional com foco na previsão de três classes finais. A fim de reduzir consumo computacional, essa abordagem usa o método de Background Subtraction, somente atualizando o status do espaço de estacionamento em que grandes níveis de movimento são detectados. O modelo proposto apresenta 94 porcento da precisão no domínio projetado

MODIFICATION OF ALEXNET ARCHITECTURE FOR DETECTION OF CAR PARKING AVAILABILITY IN VIDEO CCTV

The difficulty of finding a parking space in public places, especially during peak hours is a problem experienced by drivers. To assist the driver in finding parking space availability, a system is needed to monitor parking availability. One study to detect the availability of parking lots utilizing CCTV. However, research on the availability of parking spaces on CCTV data has several problems, detecting parking slots that are done manually to be inefficient when applied to different parking lots. Also, research to detect the availability of parking lots using the Convolution Neural Network (CNN) method with existing architecture has many parameters. Therefore, this study proposes a system to detect the availability of car parking lots using You Only Look Once (YOLO) V3 for marking the parking space and proposed a new architecture CNN called Lite AlexNet which has few parameters than other methods to speed up the process of detecting parking space availability. The best accuracy of the marking stage using YOLO V3 is 92.31% where the weather was cloudy. For the proposed Lite AlexNet get the best time training average which is 7 second compare to other existing methods and the average accuracy in every condition is 92.33% better than other methods

Towards the development of a cost-effective Image-Sensing-Smart-Parking Systems (ISenSmaP)

Finding parking in a busy city has been a major daily problem in today’s busy life. Researchers have proposed various parking spot detection systems to overcome the problem of spending a long time searching for a parking spot. These works include a wide variety of sensors to detect the presence of a vehicle in a parking spot. These approaches are expensive to implement and ineffective in extreme weather conditions in an outdoor parking environment. As a result, a cost-effective, dependable, and time-saving parking solution is much more desirable. In this thesis, we proposed and developed an image processing-based real-time parking-spot detection system using deep-learning algorithms. In this regard, we annotated the images using the Visual Geometry Group (VGG) annotator and preprocessed the dataset using the image contrast enhancement technique that attempts to solve the illumination changes in pictures captured in an open space, followed by training the model using the Mask-R-CNN (Region-Based Convolutional Neural Network) and Faster-RCNN algorithms. ROIs (Regions of interest) are used later to determine the vacancy status of each parking spot. Our experimental results demonstrate the effectiveness of our developed parking systems as we achieved a mean Average Precision (mAP) of 0.999 for the PKLot dataset and a mAP of 0.9758 for custom datasets. Furthermore, as part of the smart parking application, we developed an Android App that can be used by the end users. Our proposed intelligent parking system is scalable, cost-effective, and to the best of our knowledge, it offers higher parking spot detection accuracy than any other solutions in this domain

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

A deep learning approach for intrusion detection in Internet of Things using bi-directional long short-term memory recurrent neural network

Internet-of-Things connects every ‘thing’ with the Internet and allows these ‘things’ to communicate with each other. IoT comprises of innumerous interconnected devices of diverse complexities and trends. This fundamental nature of IoT structure intensifies the amount of attack targets which might affect the sustainable growth of IoT. Thus, security issues become a crucial factor to be addressed. A novel deep learning approach have been proposed in this thesis, for performing real-time detections of security threats in IoT systems using the Bi-directional Long Short-Term Memory Recurrent Neural Network (BLSTM RNN). The proposed approach have been implemented through Google TensorFlow implementation framework and Python programming language. To train and test the proposed approach, UNSW-NB15 dataset has been employed, which is the most up-to-date benchmark dataset with sequential samples and contemporary attack patterns. This thesis work employs binary classification of attack and normal patterns. The experimental result demonstrates the proficiency of the introduced model with respect to recall, precision, FAR and f-1 score. The model attains over 97% detection accuracy. The test result demonstrates that BLSTM RNN is profoundly effective for building highly efficient model for intrusion detection and offers a novel research methodology

Deep Learning from Smart City Data

Rapid urbanisation brings severe challenges on sustainable development and living quality of urban residents. Smart cities develop holistic solutions in the field of urban ecosystems using collected data from different types of Internet of Things (IoT) sources. Today, smart city research and applications have significantly surged as consequences of IoT and machine learning technological enhancement. As advanced machine learning methods, deep learning techniques provide an effective framework which facilitates data mining and knowledge discovery tasks especially in the area of computer vision and natural language processing. In recent years, researchers from various research fields attempted to apply deep learning technologies into smart city applications in order to establish a new smart city era. Much of the research effort on smart city has been made, for example, intelligence transportation, smart healthcare, public safety, etc. Meanwhile, we still face a lot of challenges as the deep learning techniques are still premature for smart city. In this thesis, we first provide a review of the latest research on the convergence of deep learning and smart city for data processing. The review is conducted from two perspectives: while the technique-oriented view presents the popular and extended deep learning models, the application-oriented view focuses on the representative application domains in smart cities. We then focus on two areas, which are intelligence transportation and social media analysis, to demonstrate how deep learning could be used in real-world applications by addressing some prominent issues, e.g., external knowledge integration, multi-modal knowledge fusion, semi-supervised or unsupervised learning, etc. In intelligent transportation area, an attention-based recurrent neural network is proposed to learn from traffic flow readings and external factors for multi-step prediction. More specifically, the attention mechanism is used to model the dynamic temporal dependencies of traffic flow data and a general fusion component is designed to incorporate the external factors. For the traffic event detection task, a multi-modal Generative Adversarial Network (mmGAN) is designed. The proposed model contains a sensor encoder and a social encoder to learn from both traffic flow sensor data and social media data. Meanwhile, the mmGAN model is extended to a semi-supervised architecture by leveraging generative adversarial training to further learn from unlabelled data. In social media analysis area, three deep neural models are proposed for crisis-related data classification and COVID-19 tweet analysis. We designed an adversarial training method to generate adversarial examples for image and textual social data to improve the robustness of multi-modal learning. As most social media data related to crisis or COVID-19 is not labelled, we then proposed two unsupervised text classification models on the basis of the state-of-the-art BERT model. We used the adversarial domain adaptation technique and the zero-shot learning framework to extract knowledge from a large amount of unlabeled social media data. To demonstrate the effectiveness of our proposed solutions for smart city applications, we have collected a large amount of real-time publicly available traffic sensor data from the California department of transportation and social media data (i.e., traffic, crisis and COVID-19) from Twitter, and built a few datasets for examining prediction or classification performances. The proposed methods successfully addressed the limitations of existing approaches and outperformed the popular baseline methods on these real-world datasets. We hope the work would move the relevant research one step further in creating truly intelligence for smart cities

Intelligent Parking Assist for Trucks with Prediction

Truck parking has been identified as a major issue both in the USA and E.U. and has been selected by the American Transportation Research Institute (ATRI) as the most important research need for the trucking industry in 2015 [1]\u2013[5]. The lack of appropriate and convenient parking locations has been the cause of several safety issues over the past years as drivers might be forced to either drive while tired and increase the risk of accidents or park illegally in unsafe locations, which might also pose a safety hazard to them and other drivers. Additionally, the parking shortage also impacts the shipment costs and the environment as the drivers might spend more fuel looking for parking or idling for power when parked in inappropriate locations. The project\u2019s objective is to study the truck parking problem, generate useful information and parking assist algorithms that could assist truck drivers in better planning their trips. By providing information about parking availability to truck drivers, the authors expect to induce them to better distribute themselves among existing rest areas. This would decrease the peak demand in the most popular truck stops and attenuate the problems caused by the parking shortage. In this project, several parking availability prediction algorithms are tested using data from a company\u2019s private truck stops reservation system. The prediction MSE (mean squared error) and classification (full/available) sensitivity and specificity plots are evaluated for different experiments. It is shown that none of the tested algorithms is absolutely better than the others and has superior performance in all situations. The results presented show that a more efficient way would be to combine them and use the most appropriate one according to the situation. A model assignment according to current time of the day and target time for prediction is proposed based on the experiment data