This applied research Project presents the methodology of development understood in the achievement of a tracking solution for vehicles with GPS devices, the proposed software is part of a service of vehicle tracking offered by a telecommunications company. The system’s architecture is described and specified around the use cases in the multiples disciplines of the software process development. Although, there are multiples vehicle tracking solutions which are used in differents control centers, the offered solution gather the most representative functionalities from the best tracking solutions in the market and present it on objective way through in a complete and punctual proposal where the final user can use the solution using a web browser, likewise is shown the design of the solution using the disciplines and patterns requested by the software engineering. / Este proyecto de investigación aplicada presenta la metodología de desarrollo comprendida en la realización de una solución de monitoreo para vehículos con dispositivos GPS, el software propuesto forma parte del servicio de monitoreo vehicular que ofrece una empresa de telecomunicaciones. Se describe y especifica la arquitectura del sistema a través de casos de uso en las diversas disciplinas del proceso de desarrollo de software. Si bien existen diversas soluciones de monitoreo vehicular que son usadas en distintos centros de control, la solución propuesta agrupa las funcionalidades representativas de las mejores soluciones de monitoreo del mercado y las presenta de manera objetiva a través de una propuesta completa y puntual donde el usuario final pueda dar uso de la solución a través de un navegador web, así mismo se muestra el diseño de la solución utilizando las disciplinas y patrones que exige la ingeniería de software.

Esta pesquisa propõe uma abordagem genérica de processamento de dados de rastreamento de veículos de carga com a finalidade de extrair indicadores de desempenho logístico no contexto urbano. Tais indicadores são importantes para caracterizar como as operações logísticas se desenvolvem nas cidades e subsidiar o processo de tomada de decisão dentro do âmbito do planejamento urbano de cargas, por parte do poder público. Esta abordagem é aplicada a três bases de dados - para a Região Metropolitana de São Paulo (RMSP) - obtidas junto a empresas privadas distintas, sendo duas grandes redes varejistas e a terceira uma provedora de serviços de mapas. Apesar de algumas limitações nos dados disponíveis, foi possível identificar paradas dos caminhões e analisar suas características, como: distribuição espacial e temporal, frequência de paradas por veículo, a localização de clusters de paradas etc. Além disso, foi possível classificar do os veículos que circulam na cidade distinguindo a parcela que corresponde ao \'fluxo de passagem\", veículos que estão apenas cruzando a cidade (São Paulo) e, por fim, apresentou-se também um estudo do perfil de velocidades - por hora e dia da semana - em uma importante via arterial de São Paulo. Os resultados e análises desta pesquisa reforçam que há grande potencial na utilização de dados de rastreamento de veículos de carga no contexto do planejamento de transporte urbano de cargas, embora este dependa em parte de algumas especificidades dos dados, como a frequência dos registros, a precisão da posição geográfica coletada, além da necessidade de um processamento otimizado devido ao grande volume de dados. / The purpose of this research is a general approach for processing tracking data of cargo vehicles in order to extract logistic performance indicators in the urban scenario, essential to characterize how logistic operations are developed and support the decision-making process within the scope of urban freight planning by the public sector. This approach is applied to three data bases - for the São Paulo Metropolitan Region (SPMR) - acquired from different private companies, being two large retailers and the third a great mapping services provider. In spite of available data limitations, it was possible to identity truck stops and analyze its particulars, such as: spatial and temporal distribution, stop frequency per vehicle, the location of stop clusters, etc. Apart from that, it was possible to classify among the flow of vehicles in the city, the ones that are just passing, crossing the city, and ultimately, it was presented a speed profile analysis - per day and week day - of an important arterial way of São Paulo. The results and analysis of this research reinforce that there is great potential in the use of cargo vehicle tracking data for urban freight planning, even though it partially depends on some data specifics, such as frequency of records, location accuracy, besides the need of an optimized data processing due to their great volume.

License plate recognition (LPR) technology has been widely applied in many different transportation applications such as enforcement, vehicle monitoring and access control. In most applications involving enforcement (e.g. cashless toll collection, congestion charging) and access control (e.g. car parking) a plate is recognized at one location (or checkpoint) and compared against a list of authorized vehicles. In this research I dealt with applications where a vehicle is detected at two locations and there is no list of reference for vehicle identification. There seems to be very little effort in the past to exploit all information generated by LPR systems. In nowadays, LPR machines have the ability to recognize most characters on the vehicle plates even under the harshest practical conditions. Therefore, even though the equipment are not perfect in terms of plate reading, it is still possible to judge with certain confidence if a pair of imperfect readings, in the form of sequenced characters (strings), most likely belong to the same vehicle. The challenge here is to design a matching procedure in order to decide whether or not they belong to same vehicle. In view of the aforementioned problem, this research intended to design and assess a matching procedure that takes advantage of a similarity measure called edit distance (ED) between two strings. The ED measure the minimum editing cost to convert a string to another. The study first attempted to assess a simple case of a dual LPR setup using the traditional ED formulation with 0 or 1 cost assignments (i.e. 0 if a pair-wise character is the same, and 1 otherwise). For this dual setup, this research has further proposed a symbol-based weight function using a probabilistic approach having as input parameters the conditional probability matrix of character association. As a result, this new formulation outperformed the original ED formulation. Lastly, the research sought to incorporate the passage time information into the procedure. With this, the performance of the matching procedure improved considerably resulting in a high positive matching rate and much lower (about 2%) false matching rate.

Este trabalho apresenta um novo método para o rastreamento e contagem de veículos em vídeos de tráfego urbano. Usando técnicas de processamento de imagens e de agrupamentos de partículas, o método proposto usa coerência de movimento e coerência espacial para agrupar partículas, de modo que cada grupo represente veículos nas sequências de vídeo. Uma máscara contendo os objetos do primeiro plano é criada usando os métodos Gaussian Mixture Model e Motion Energy Images para determinar os locais onde as partículas devem ser geradas, e as regiões convexas dos agrupamentos são então analisadas para verificar se correspondem a um veículo. Esta análise leva em consideração a forma convexa dos grupos de partículas (objetos) e a máscara de foreground para realizar a fusão ou divisão dos agrupamentos obtidos. Depois que um veículo é identificado, ele é rastreado utilizando similaridade de histogramas de cor em janelas centradas nas partículas dos agrupamentos. A contagem de veículos acontece em laços virtuais definidos pelo usuário, através da interseção dos veículos rastreados com os laços virtuais. Testes foram realizados utilizando seis diferentes vídeos de tráfego, em um total de 80000 quadros. Os resultados foram comparados com métodos semelhantes disponíveis na literatura, fornecendo, resultados equivalentes ou superiores. / This work presents a new method for tracking and counting vehicles in traffic videos. Using techniques of image processing and particle clustering, the proposed method uses motion coherence and spatial adjacency to group particles so that each group represents vehicles in the video sequences. A foreground mask is created using Gaussian Mixture Model and Motion Energy Images to determine the locations where the particles must be generated, and the convex shapes of detecting groups are then analyzed for the potential detection of vehicles. This analysis takes into consideration the convex shape of the particle groups (objects) and the foreground mask to merge or split the obtained groupings. After a vehicle is identified, it is tracked using the similarity of color histograms on windows centered at the particle locations. The vehicle count takes place on userdefined virtual loops, through the intersections of tracked vehicles with the virtual loops. Tests were conducted using six different traffic videos, on a total of 80.000 frames. The results were compared with similar methods available in the literature, providing results equivalent or superior.

Este trabalho apresenta um novo método para o rastreamento e contagem de veículos em vídeos de tráfego urbano. Usando técnicas de processamento de imagens e de agrupamentos de partículas, o método proposto usa coerência de movimento e coerência espacial para agrupar partículas, de modo que cada grupo represente veículos nas sequências de vídeo. Uma máscara contendo os objetos do primeiro plano é criada usando os métodos Gaussian Mixture Model e Motion Energy Images para determinar os locais onde as partículas devem ser geradas, e as regiões convexas dos agrupamentos são então analisadas para verificar se correspondem a um veículo. Esta análise leva em consideração a forma convexa dos grupos de partículas (objetos) e a máscara de foreground para realizar a fusão ou divisão dos agrupamentos obtidos. Depois que um veículo é identificado, ele é rastreado utilizando similaridade de histogramas de cor em janelas centradas nas partículas dos agrupamentos. A contagem de veículos acontece em laços virtuais definidos pelo usuário, através da interseção dos veículos rastreados com os laços virtuais. Testes foram realizados utilizando seis diferentes vídeos de tráfego, em um total de 80000 quadros. Os resultados foram comparados com métodos semelhantes disponíveis na literatura, fornecendo, resultados equivalentes ou superiores. / This work presents a new method for tracking and counting vehicles in traffic videos. Using techniques of image processing and particle clustering, the proposed method uses motion coherence and spatial adjacency to group particles so that each group represents vehicles in the video sequences. A foreground mask is created using Gaussian Mixture Model and Motion Energy Images to determine the locations where the particles must be generated, and the convex shapes of detecting groups are then analyzed for the potential detection of vehicles. This analysis takes into consideration the convex shape of the particle groups (objects) and the foreground mask to merge or split the obtained groupings. After a vehicle is identified, it is tracked using the similarity of color histograms on windows centered at the particle locations. The vehicle count takes place on userdefined virtual loops, through the intersections of tracked vehicles with the virtual loops. Tests were conducted using six different traffic videos, on a total of 80.000 frames. The results were compared with similar methods available in the literature, providing results equivalent or superior.

Este trabalho apresenta um novo método para o rastreamento e contagem de veículos em vídeos de tráfego urbano. Usando técnicas de processamento de imagens e de agrupamentos de partículas, o método proposto usa coerência de movimento e coerência espacial para agrupar partículas, de modo que cada grupo represente veículos nas sequências de vídeo. Uma máscara contendo os objetos do primeiro plano é criada usando os métodos Gaussian Mixture Model e Motion Energy Images para determinar os locais onde as partículas devem ser geradas, e as regiões convexas dos agrupamentos são então analisadas para verificar se correspondem a um veículo. Esta análise leva em consideração a forma convexa dos grupos de partículas (objetos) e a máscara de foreground para realizar a fusão ou divisão dos agrupamentos obtidos. Depois que um veículo é identificado, ele é rastreado utilizando similaridade de histogramas de cor em janelas centradas nas partículas dos agrupamentos. A contagem de veículos acontece em laços virtuais definidos pelo usuário, através da interseção dos veículos rastreados com os laços virtuais. Testes foram realizados utilizando seis diferentes vídeos de tráfego, em um total de 80000 quadros. Os resultados foram comparados com métodos semelhantes disponíveis na literatura, fornecendo, resultados equivalentes ou superiores. / This work presents a new method for tracking and counting vehicles in traffic videos. Using techniques of image processing and particle clustering, the proposed method uses motion coherence and spatial adjacency to group particles so that each group represents vehicles in the video sequences. A foreground mask is created using Gaussian Mixture Model and Motion Energy Images to determine the locations where the particles must be generated, and the convex shapes of detecting groups are then analyzed for the potential detection of vehicles. This analysis takes into consideration the convex shape of the particle groups (objects) and the foreground mask to merge or split the obtained groupings. After a vehicle is identified, it is tracked using the similarity of color histograms on windows centered at the particle locations. The vehicle count takes place on userdefined virtual loops, through the intersections of tracked vehicles with the virtual loops. Tests were conducted using six different traffic videos, on a total of 80.000 frames. The results were compared with similar methods available in the literature, providing results equivalent or superior.

Yes / The issue of asset tracking in dense environments where the performance of the global positioning system (GPS) becomes unavailable or unreliable is addressed. The proposed solution uses a low-profile array of antenna elements (sensors) mounted on a finite conducting ground. A compact-size sensor array of six electrically small dual-band omnidirectional spiral antenna elements was designed as a front end of a tracker to operate in the 402 and 837 MHz spectrum bands. For the lower band, a three-element superposition method is applied to support estimation of the angle of arrival (AOA), whereas all six sensors are employed for the higher band. A low complexity and accurate AOA determination algorithm is proposed, the projection vector (PV), and this is combined with the array mentioned. Orthogonal frequency division multiplexing (OFDM) is integrated with the PV technique to increase the estimation resolution. The system was found to be suitable for installation on the roof of vehicles to localize the position of assets. The proposed system was tested for the tracking of nonstationary sources, and then two scenarios were investigated using propagation modeling software: outdoor to outdoor and outdoor to indoor. The results confirm that the proposed tracking system works efficiently with a single snapshot. / European Union Horizon 2020 Research and Innovation Program; 10.13039/501100009928 - Higher Committee for Education Development (HCED), Iraq

In this thesis, multiple on-road vehicle tracking problem is explored, with greater consideration of road constraints and interactions between vehicles. A comprehensive method for tracking multiple on-road vehicles is proposed by making use of domain knowledge of on-road vehicle motion. Starting with raw measurements provided by sensors, bias correction methods for sensors commonly used in vehicle tracking are briefly introduced and a fast but effective bias correction method for airborne video sensor is proposed. In the proposed method, by assuming errors in sensor parameter measurements are close to zero, the bias is separately addressed in converted measurements of target position by a linear term of errors in sensor parameter measurements. Based on this model, the bias is efficiently estimated by addressing it while tracking or using measurements of targets that are observed by multiple airborne video sensors simultaneously. The proposed method is compared with other airborne video bias correction methods through simulations. The numerical results demonstrate the effectiveness of the proposed method for correcting bias as well as its high computational efficiency. Then, a novel tracking algorithm that utilizes domain knowledge of on-road vehicle motion, i.e., road-map information and interactions among vehicles, by integrating a car-following model into a road coordinate system, is proposed for tracking multiple vehicles on single-lane roads. This algorithm is extended for tracking multiple vehicles on multi-lane roads: The road coordinate system is extended to two-dimension to express lanes on roads and a lane-changing model is integrated for modeling lane-changing behavior of vehicles. Since the longitudinal and lateral motions are mutually dependent, the longitudinal and lateral states of vehicles are estimated sequentially in a recursive manner. Two estimation strategies are proposed: a) The unscented Kalman filter combined with the multiple hypothesis tracking framework to estimate longitudinal and lateral states of vehicles, respectively. b) A unified particle filter framework with a specifically designed computationally-efficient joint sampling method to estimate longitudinal and lateral states of vehicles jointly. Both of two estimation methods can handle unknown parameters in motion models. A posterior Cramer-Rao lower bound is derived for quantifying achievable estimation accuracy in both single-lane and multi-lane cases, respectively. Numerical results show that the proposed algorithms achieve better track accuracy and consistency than conventional multi-vehicle tracking algorithms, which assumes that vehicles move independently of one another. / Thesis / Doctor of Philosophy (PhD)

The goal of this work is a design and implementation of an on-board driver’s assistance and assessment system. The system overcomes the problem that typical evaluation of skills is performed by experts who may be subjective and are able to consider only a limited number of factors and indicators. The proposed solution is based on eight indicators, which are associated with the vehicle’s speed, acceleration, jerk, engine rotational speed and driving time. These indicators are used to estimate three driving style criteria: safety, economy and comfort. The comprehensive evaluation is done by merging all indicators into one final score. The system is designed according to User-Centred Design method and follows Internet of Things concept. Raspberry Pi minicomputer is used as a central unit to acquire and store the data during the ride and sending them to a server using GSM network. OBD-II interface is used to obtain the data from the vehicle’s network and GPS and accelerometer modules to acquire additional information. MATLAB environment on a local PC is used to process collected data. An outline of the measurements available from ODB-II interface depending on a car model is made. The proposed system has been implemented and evaluated. The evaluation, conducted by collecting readings for specific road actions at different speeds and with different dynamics, confirms that the chosen indicators reliably represent driver’s behaviour. The system was experimentally validated on a group of drivers. The obtained results prove the system’s ability to quantitatively distinguish different driving styles. The system's stability and usability were verified on long-route test. Moreover, the used spider diagram approach established a convenient visualization platform for multidimensional comparison of the result and comprehensive assessment in an intelligible manner. Overall conclusion is that the developed system is a reliable method of the drivers’ behaviour evaluation.

Esta dissertação apresenta o desenvolvido de um método, baseado em agrupamento de partículas, para realizar a contagem de veículos em vídeos de tráfego. Tal procedimento é importante em sistemas de tráfego inteligentes, ou como uma ferramenta auxiliar no planejamento de vias urbanas. Utilizando técnicas de processamento de imagens e agrupamento de partículas, o método proposto utiliza-se da coerência de movimento e posição espacial existente entre partículas extraídas das imagens de vídeo para agrupá-las, formando figuras convexas que são analisadas em busca de possíveis veículos. Essa análise leva em consideração a morfologia das figuras convexas e a informação de fundo da imagem, para unir ou dividir os agrupamentos. Após a identificação de um veículo, o mesmo é rastreado utilizando-se similaridade de histograma de cores, aplicado em janelas centradas nas partículas. A contagem dos veículos ocorre em laços virtuais definidos pelo usuário nas pistas desejadas, através da intersecção das figuras convexas rastreadas com estes laços virtuais. Testes foram realizados utilizando-se vídeos de seis cenas diferentes, totalizando 81.000 quadros. Os resultados das contagens de veículos obtidos foram comparados a dois métodos atuais. Um método possui abordagem similar ao método proposto (KIM, 2008), que tenta fixar agrupamentos de partículas em formas elipsoidais. O outro método (SÁNCHEZ et al., 2011) rastreia objetos conectados, quando estes são diferentes do fundo, através da intersecção destes objetos entre quadros adjacentes. Considerando-se o universo total de veículos analisados, 1085 veículos, os resultados obtidos pelo método proposto apresentaram uma diferença absoluta na contagem dos veículos intermediária aos métodos comparativos, 53 veículos contra 66 e 27 para (KIM, 2008) e (SÁNCHEZ et al., 2011) respectivamente, sendo o único método que contou menos veículos que o valor real, enquanto os métodos comparativos contaram veículos além do valor real. O método proposto perde 102 veículos, valor inferior ao método de (SÁNCHEZ et al., 2011), 181, e praticamente o mesmo número que o método de (KIM, 2008), 101. Já os veículos detectados mais de uma vez apresentam valores inferiores para o método proposto, 49, em relação aos métodos comparativos, 167 para (KIM, 2008) e 208 para (SÁNCHEZ et al., 2011). / This dissertation presents the developed of a method based on particle group, to conduct the count of vehicles in traffic videos. This procedure is important in intelligent traffic systems, or as an auxiliary tool in the planning of urban streets. Using image processing techniques and grouping of particles, the proposed method uses the coherence of spatial position and movement between particles extracted from the video footage to assemble them into convex figures that are parsed in search of possible vehicles. This analysis takes into account the morphology of convex figures and background information of the image, to merge or split the groups. After the identification of a vehicle, it is tracked using color histogram similarity, applied in Windows centered on particles. The count of vehicles occurs in user-defined virtual loops on the tracks desired, through the intersection of convex figures traced with these virtual loops. Tests were performed using videos of six different scenes, totaling 81,000 frames. The results of vehicle counts obtained were compared to two current methods. A method has similar approach to proposed method (KIM, 2008), which attempts to establish groups of particles in ellipsoidal shapes. The other method (SÁNCHEZ et al., 2011) tracks connected objects, when these are different from the background, through the intersection of these objects between adjacent frames. Considering the total universe of vehicles examined, 1085, the results obtained by the proposed method showed an intermediate absolute difference in counting of vehicles to comparative methods, 53 against 66 and 27 vehicles for (KIM, 2008) and (SÁNCHEZ et al., 2011) respectively. The proposed method is the only one that counted vehicles less than the real value, while comparative methods counted vehicles beyond the real value. The proposed method loses 102 vehicles, lower than value to (SÁNCHEZ et al., 2011), 181, and roughly the same number as the method of (KIM, 2008), 101. The number of vehicles detected more than once are lower for the proposed method in relation to the comparative methods, 49 vehicles against 167 to (Kim, 2008) and 208 to (SANCHEZ et al., 2011).