Automatic Identification of Personal Automobiles Plates of Iran Using Genetic Algorithm

In this study, a new method for using LPR systems for Iranian plates number has been presented. Increasing the precision of the letter recognition process and reducing the amount of training are in fact the main advantages of the new hybrid model. The K-NN has been implemented as the first classification method, because it was simple, and it was resistant to the noisy data, and for large datasets it is also effective at zero cost. The confusion problem related to the similarity of letters in plate numbers has also been resolved by using the classification model of the multi-class genetic algorithm. The genetic algorithm improves K-NN performance in the recognition of similar letters. Vehicle license plate recognition (LPR) plays an important role in ITS and is mainly used in access control systems.The purpose of this research is to determine the Iranian plate automobiles that are specifically owned by the automobile. The confusion caused by the similarity between the letters of the alphabet and numeric characters is one of the problems of the Persian LPR systems at the diagnostic stage. In this regard, a method using the KNN-based advantages of genetic algorithm as a hybrid model is presented in this study to overcome the above problem. The genetic algorithm has been trained and tested only with the same letters, thus the cost of training for the genetic algorithm has significantly decreased. Comparison of the results obtained from the experiments carried out in this study with the results of a similar study shows that the combined KNN-genetic algorithm model significantly improved the detection rate of the letters for all tested cases from 94% to 97.03% . Keywords: Coding, plate recognition, genetics, Iran automobile, Genetic Algorithm DOI: 10.7176/CEIS/10-6-04 Publication date:July 31st 2019

Microsoft COCO: Common Objects in Context

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model

Augmented Reality for the Mobile Police Force

Portuguese law enforcement organizations currently face a significant technology gap. Research has shown that some law organizations, such as Polícia Judiciária (PJ) and Polícia de Segurança Pública (PSP), often criticize the lack of technology to support the Police work in all kinds of fields, from criminality prevention to minor infractions. This study aims to determine how augmented reality (AR) technology can be used to ease/improve the day-to-day tasks of the law enforcement forces and how do the end-users perceive this new type of information. Based on a review of the literature and implementations on AR technologies to aid law enforcement organizations, a proof-of-concept smartphone application was developed in order to aid the police infraction ticket issuing process. The developed solution was analysed to infer its usability. As such, various users tests were conducted with a total of thirty users including police personnel and nonassociated users. The users were then asked to answer a questionnaire contemplating the System Usability Scale (SUS) questions. The responses were analysed and then combined with the Quantitative Evaluation Framework (QEF) in order to extrapolate the proof-of-concept’s final value. The results suggest that the ticket issuing process was fully integrated in the proofof-concept and was well received amongst the users. The system contemplates the possibility to scale to other devices other than the smartphone, for example surveillance cameras or wearables, as well as including new features to perform different tasks such as recognizing vehicles through AR and Depth.A introdução da tecnologia na sociedade alterou o paradigma de como as tarefas são realizadas. Uma grande parte dos setores económicos decidiu apostar na automatização e mecanização de processos, reduzindo assim os encargos em recursos humanos, bem como o número de erros humanos. Apesar do uso de tecnologias ser recorrente em várias áreas e serviços, as forças policiais continuam a ter o seu uso negligenciado. Muitas das tarefas policiais, como a passagem de infrações ou contra-ordenações, são feitas através da introdução manual de dados num sistema generalizado implantado num computador de bordo ou através da passagem de uma contra-ordenação por escrito que é posteriormente introduzida no sistema aquando a chegada do agente à esquadra do seu destacamento. O processo em questão acaba por ser bastante moroso, como também propenso à realização de erros humanos. O descontentamento das Forças policiais como a Polícia Judiciária (PJ) ou a Polícia de Segurança Pública (PSP) é visível nas contestações feitas relacionadas com a falta de suporte tecnológico em variadas operações policiais. Este estudo tem como objetivo determinar como as tecnologias de Augmented Reality (AR) podem ser utilizadas de modo a otimizar as tarefas policiais e como a introdução da mesma é percecionada pelos utilizadores nas tarefas em questão. A investigação foca-se no desenvolvimento de uma aplicação de AR para smartphone como uma prova de conceito com o intuito de assistir as forças policiais na passagem de infrações e contra-ordenações. Consequentemente, foi realizada uma investigação sobre a tecnologia de AR e as suas categorias. Após serem detalhadas as nuances da AR, foi efetuada uma investigação na literatura e implementações de trabalhos relacionados contendo sistemas que implementam AR com o objetivo primário de assistir as forças policiais em variadas tarefas. Desta forma, foi possível detalhar algumas das possíveis tecnologias que acabaram por ser utilizadas para o desenvolvimento da aplicação supramencionada. Aquando da finalização do estudo dos trabalhos relacionados, foi analisado o contexto de negócio da prova de conceito a desenvolver, validando a necessidade e o contexto onde o sistema desenvolvido viria ser inserido. A aplicação foi desenvolvida em Unity com recurso à framework ARFoundation, que possibilitou o incorporamento e sobreposição de dados virtuais sobre a vista real observada pelo utilizador. O sistema foi desenhado de forma a realizar a deteção automática de matrículas expondo a informação detetada na forma de componentes AR no ecrã do utilizador, possibilitando a posterior submissão de uma infração se a matrícula selecionada pelo utilizador estiver contida na base de dados. A prova de conceito é composta por seis conceitos de negócio, sendo estes: a interface gráfica para o utilizador; o módulo de Optical Character Recognition (OCR), responsável pela deteção e comparação de carateres alfanuméricos pre-registados no sistema: o Plate Recognition Training, responsável pelo aprendizagem dos contornos e localizações das matrículas; a câmara, responsável pela obtenção do vídeo em tempo real para deteção; o integration system, responsável por integrar todos os módulos supramencionados; e por último, o resources/fileSystem, responsável por armazenar todos os dados necessários para o funcionamento da aplicação. Após a implementação do sistema, o mesmo foi submetido a vários testes de utilizador com recurso a um conjunto predefinido de ações, de modo a aferir a integração e usabilidade da aplicação. Foram feitos testes com trinta utilizadores, incluindo alguns agentes policiais. Posteriormente, os utilizadores supracitados foram convidados à realização de um questionário. As respostas foram analisadas de forma a apurar o valor de usabilidade final referente à prova de conceito. Os resultados obtidos confirmam que o processo de submissão de infrações foi totalmente integrado na prova de conceito e que o sistema foi positivamente avaliado pelos utilizadores. O sistema contempla a possibilidade de ser integrado em diferentes dispositivos em adição ao smartphone, como por exemplo câmaras de videovigilância ou wearables. O sistema está ainda preparado para ser escalado e incluir novas funcionalidades para realizar diferentes tarefas policiais, como a de reconhecer veículos através de AR e Profundidade, sendo que este conceito foi brevemente explorado nesta tese

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras

An Intelligent Reconnaissance Framework for Homeland Security

The cross border terrorism and internal terrorist attacks are critical issues for any country to deal with. In India, such types of incidents that breach homeland security are increasing now a day. Tracking and combating such incidents depends only on the radio communications and manual operations of security agencies. These security agencies face various challenges to get the real-time location of the targeted vehicles, their direction of fleeing, etc. This paper proposes a novel application for automatic tracking of suspicious vehicles in real-time. The proposed application tracks the vehicle based on their registration number, type, colour and RFID tag. The proposed approach for vehicle recognition based on image processing achieves 92.45 per cent accuracy. The RFID-based vehicle identification technique achieves 100 per cent accuracy. This paper also proposes an approach for vehicle classification. The average classification accuracy obtained by the proposed approach is 93.3 per cent. An integrated framework for tracking of any vehicle at the request of security agencies is also proposed. Security agencies can track any vehicles in a specific time period by using the user interface of the application

Learning Semantic Part-Based Models from Google Images

We propose a technique to train semantic part-based models of object classes from Google Images. Our models encompass the appearance of parts and their spatial arrangement on the object, specific to each viewpoint. We learn these rich models by collecting training instances for both parts and objects, and automatically connecting the two levels. Our framework works incrementally, by learning from easy examples first, and then gradually adapting to harder ones. A key benefit of this approach is that it requires no manual part location annotations. We evaluate our models on the challenging PASCAL-Part dataset [1] and show how their performance increases at every step of the learning, with the final models more than doubling the performance of directly training from images retrieved by querying for part names (from 12.9 to 27.2 AP). Moreover, we show that our part models can help object detection performance by enriching the R-CNN detector with parts

Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.Comment: 14 page

A Global Archive of Coseismic DInSAR Products Obtained Through Unsupervised Sentinel-1 Data Processing

We present an automatic and unsupervised tool for the systematic generation of Sentinel-1 (S1) differential synthetic aperture radar interferometry (DInSAR) coseismic products. In particular, the tool first retrieves the location, depth, and magnitude of every seismic event from interoperable online earthquake catalogs (e.g., the United States Geological Survey (USGS) and the Italian National Institute of Geophysics and Volcanology (INGV) and then, for significant (with respect to a set of selected thresholds) earthquakes, it automatically triggers the downloading of S1 data and their interferometric processing over the area affected by the earthquake. The automatic system we developed has also been implemented within a Cloud-Computing (CC) environment, specifically the Amazon Web Services, with the aim of creating a global database of DInSAR S1 coseismic products, which consist of displacement maps and the associated wrapped interferograms and spatial coherences. This information will progressively be made freely available through the European Plate Observing System (EPOS) Research Infrastructure, thus providing the scientific community with a large catalog of DInSAR data that can be helpful for investigating the dynamics of surface deformation in the seismic zones around the Earth. The developed tool can also support national and local authorities during seismic crises by quickly providing information on the surface deformation induced by earthquakes