Object tracking is an active research topic in computer vision and has appli- cation in several areas, such as event detection and robotics. Vehicle tracking is used in Intelligent Transport System (ITS) and surveillance systems. Its re- liability is critical to the overall performance of these systems. Feature-based methods that are used to represent distinctive content in visual frames are one approach to vehicle tracking. Existing feature-based tracking systems can only track vehicles under ideal conditions. They have difficulties when used under a variety of conditions, for example, during both the day and night. They are highly dependent on stable local features that can be tracked for a long time period. These local features are easily lost because of their local property and image noise caused by factors such as, headlight reflections and sun glare. This thesis presents a new approach, addressing the reliability issues mentioned above, tracking whole feature groups composed of feature points extracted with the Scale Invariant Feature Transform (SIFT) algorithm. A feature group in- cludes several features that share a similar property over a time period and can be tracked to the next frame by tracking individual feature points inside it. It is lost only when all of the features in it are lost in the next frame. We cre- ate these feature groups by clustering individual feature points using distance, velocity and acceleration information between two consecutive frames. These feature groups are then hierarchically clustered by their inter-group distance, velocity and acceleration information. Experimental results show that the pro- posed vehicle tracking system can track vehicles with the average accuracy of over 95%, even when the vehicles have complex motions in noisy scenes. It gen- erally works well even in difficult environments, such as for rainy days, windy days, and at night. We are surprised to find that our tracking system locates and tracks motor bikes and pedestrians. This could open up wider opportunities and further investigation and experiments are required to confirm the tracking performance for these objects. Further work is also required to track more com- plex motions, such as rotation and articulated objects with different motions on different parts.

Object tracking is an active research topic in computer vision and has appli- cation in several areas, such as event detection and robotics. Vehicle tracking is used in Intelligent Transport System (ITS) and surveillance systems. Its re- liability is critical to the overall performance of these systems. Feature-based methods that are used to represent distinctive content in visual frames are one approach to vehicle tracking. Existing feature-based tracking systems can only track vehicles under ideal conditions. They have difficulties when used under a variety of conditions, for example, during both the day and night. They are highly dependent on stable local features that can be tracked for a long time period. These local features are easily lost because of their local property and image noise caused by factors such as, headlight reflections and sun glare. This thesis presents a new approach, addressing the reliability issues mentioned above, tracking whole feature groups composed of feature points extracted with the Scale Invariant Feature Transform (SIFT) algorithm. A feature group in- cludes several features that share a similar property over a time period and can be tracked to the next frame by tracking individual feature points inside it. It is lost only when all of the features in it are lost in the next frame. We cre- ate these feature groups by clustering individual feature points using distance, velocity and acceleration information between two consecutive frames. These feature groups are then hierarchically clustered by their inter-group distance, velocity and acceleration information. Experimental results show that the pro- posed vehicle tracking system can track vehicles with the average accuracy of over 95%, even when the vehicles have complex motions in noisy scenes. It gen- erally works well even in difficult environments, such as for rainy days, windy days, and at night. We are surprised to find that our tracking system locates and tracks motor bikes and pedestrians. This could open up wider opportunities and further investigation and experiments are required to confirm the tracking performance for these objects. Further work is also required to track more com- plex motions, such as rotation and articulated objects with different motions on different parts.

La presente tesis tiene por finalidad verificar las condiciones de seguridad en el diseño de curvas horizontales de vía utilizando esta herramienta de vanguardia de Autodesk: Vehicle Tracking y AutoCAD Civil 3D versión 2015 en la cual se ha utilizado los parámetros de la norma peruana vigente (DG-2013) publicada por el MTC (Ministerio de Transportes y Comunicaciones). Este software nos permite simular las maniobras de giro de vehículos en proyectos de ingeniería civil y de transporte. Esencial para un diseño preciso y rentable de intersecciones, glorietas, terminales de transporte y áreas de carga y descarga, es un activo para cualquier proyecto que involucre el diseño de accesos de vehículos, gálibos (Marca o luces que señala las dimensiones máximas permitidas a un vehículo para el paso por un túnel o un puente) y maniobrabilidad. Para la explicación de esta tesis se han elegido tres casos para las simulaciones uno de ellos consta parte del proyecto ejecutado del Plan Binacional de Desarrollo de la Región Fronteriza Perú – Ecuador cuya ejecución fue prevista en un periodo de 12 años (2000-2009). Entre estos destacan cinco ejes viales binacionales que tienen por objetivos conformar una red de conexión de la zona fronteriza común en este caso se ha elegido el mejoramiento y Rehabilitación de la carretera Sullana – El Alamor eje vial N°02 de la interconexión vial Perú – Ecuador. El siguiente caso se refiere al Ovalo Gutiérrez también conocido como rotonda, glorieta o redondela es una intersección de carreteras (rutas), avenidas o calles que se confluyen están conectadas entre sí mediante un anillo. Estas minimizan el riesgo de accidente ya que fuerzan a reducir la velocidad El tercer caso se refiere a las intersecciones urbanas ofrecen un potencial de investigación en diseño geométrico de vías. En los últimos años, la demanda vial ha crecido por el aumento del número de vehículos automotores, se puede decir que la oferta es bastante inferior a la demanda de tránsito. Esto ha traído como consecuencia incrementos en la congestión, demoras, accidentes y problemas ambientales, bastante mayores que los considerados aceptables. Contamos con la capacidad, la topografía, los conocimientos, las condiciones de mejorar la vida de la malla vial y también con la necesidad de disponer de un instrumento idóneo para afrontar la solución de la actual problemática. Finalmente como aporte de la tesis verificaremos si los sobreanchos según la norma peruana. Si bien es cierto que la velocidad de diseño que es la máx. velocidad que se puede tomar para permitir garantizar seguridad y comodidad el cual va directamente relacionado con el cálculo y elección del radio mínimo utilizando la tabla 302.02 de la DG-2013.(Ministerio de Transportes y. , Agosto, 2014)

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / Vehicle tracking system based on GPS has been paid more and more attention. The system consists of GIS (Geological Information System), master station, movable station and communication network. Movable stations installed on automobiles transmit their position and status messages to the master station. All vehicles' tracks are drawn on the electrical map displayed by the master station's computer screen in real time. Vehicles' alarming signals can also be transmitted to the master station simultaneously. This paper presents a whole designing scheme of the vehicle tracking system, then it makes a thorough introduction to the system's performance and working procedure. The key technologies employed by the system and the relations between them are also discussed in details in the paper.

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This thesis is concerned with the challenging tasks of automatic and real-time vehicle detection and tracking from aerial video. The aim of this thesis is to build an automatic system that can accurately localise any vehicles that appear in aerial video frames and track the target vehicles with trackers. Vehicle detection and tracking have many applications and this has been an active area of research during recent years; however, it is still a challenge to deal with certain realistic environments. This thesis develops vehicle detection and tracking algorithms which enhance the robustness of detection and tracking beyond the existing approaches. The basis of the vehicle detection system proposed in this thesis has different object categorisation approaches, with colour and texture features in both point and area template forms. The thesis also proposes a novel Self-Learning Tracking and Detection approach, which is an extension to the existing Tracking Learning Detection (TLD) algorithm. There are a number of challenges in vehicle detection and tracking. The most difficult challenge of detection is distinguishing and clustering the target vehicle from the background objects and noises. Under certain conditions, the images captured from Unmanned Aerial Vehicles (UAVs) are also blurred; for example, turbulence may make the vehicle shake during flight. This thesis tackles these challenges by applying integrated multiple feature descriptors for real-time processing. In this thesis, three vehicle detection approaches are proposed: the HSV-GLCM feature approach, the ISM-SIFT feature approach and the FAST-HoG approach. The general vehicle detection approaches used have highly flexible implicit shape representations. They are based on training samples in both positive and negative sets and use updated classifiers to distinguish the targets. It has been found that the detection results attained by using HSV-GLCM texture features can be affected by blurring problems; the proposed detection algorithms can further segment the edges of the vehicles from the background. Using the point descriptor feature can solve the blurring problem, however, the large amount of information contained in point descriptors can lead to processing times that are too long for real-time applications. So the FAST-HoG approach combining the point feature and the shape feature is proposed. This new approach is able to speed up the process that attains the real-time performance. Finally, a detection approach using HoG with the FAST feature is also proposed. The HoG approach is widely used in object recognition, as it has a strong ability to represent the shape vector of the object. However, the original HoG feature is sensitive to the orientation of the target; this method improves the algorithm by inserting the direction vectors of the targets. For the tracking process, a novel tracking approach was proposed, an extension of the TLD algorithm, in order to track multiple targets. The extended approach upgrades the original system, which can only track a single target, which must be selected before the detection and tracking process. The greatest challenge to vehicle tracking is long-term tracking. The target object can change its appearance during the process and illumination and scale changes can also occur. The original TLD feature assumed that tracking can make errors during the tracking process, and the accumulation of these errors could cause tracking failure, so the original TLD proposed using a learning approach in between the tracking and the detection by adding a pair of inspectors (positive and negative) to constantly estimate errors. This thesis extends the TLD approach with a new detection method in order to achieve multiple-target tracking. A Forward and Backward Tracking approach has been proposed to eliminate tracking errors and other problems such as occlusion. The main purpose of the proposed tracking system is to learn the features of the targets during tracking and re-train the detection classifier for further processes. This thesis puts particular emphasis on vehicle detection and tracking in different extreme scenarios such as crowed highway vehicle detection, blurred images and changes in the appearance of the targets. Compared with currently existing detection and tracking approaches, the proposed approaches demonstrate a robust increase in accuracy in each scenario.

We present a novel approach for vehicle detection and tracking in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to form an unsupervised system, where vehicles are automatically "learned" from video sequences. First an enhanced adaptive background mixture model is used to identify positive and negative examples. Then a video-specific classifier is trained with these examples. Both the background model and the trained classifier are used in conjunction to detect vehicles in a frame. Tracking is achieved by a simplified multi-hypotheses approach. An over-complete set of tracks is created considering every observation within a time interval. As needed hypothesized detections are generated to force continuous tracks. Finally, a scoring function is used to separate the valid tracks in the over-complete set. The proposed detection and tracking algorithm is tested in a challenging application; vehicle counting. Our method achieved very accurate results in three traffic surveillance videos that are significantly different in terms of view-point, quality and clutter. / text

Vehicle location and tracking has a variety of commercial applications and none of the techniques currently used can provide accurate results in all situations. This thesis details a preliminary investigation into a new location estimation method which uses optical environmental data, gathered by the vehicle during motion, to locate and track vehicle positions by comparing said data to pre-recorded optical maps of the intended location space. The design and implementation of an optical data recorder is presented. The map creation process is detailed and the location algorithm, based on a particle filter, is described in full. System tests were performed offline on a desktop PC using real world data collected by the data recorder and their results are presented. These tests show good performance for the system tracking the vehicle once its approximate location is determined. However locating a vehicle from scratch appears to be infeasible in a realistically large location space.

La presente tesis tiene por finalidad verificar las condiciones de seguridad en el diseño de curvas horizontales de vía utilizando esta herramienta de vanguardia de Autodesk: Vehicle Tracking y AutoCAD Civil 3D versión 2015 en la cual se ha utilizado los parámetros de la norma peruana vigente (DG-2013) publicada por el MTC (Ministerio de Transportes y Comunicaciones). Este software nos permite simular las maniobras de giro de vehículos en proyectos de ingeniería civil y de transporte. Esencial para un diseño preciso y rentable de intersecciones, glorietas, terminales de transporte y áreas de carga y descarga, es un activo para cualquier proyecto que involucre el diseño de accesos de vehículos, gálibos (Marca o luces que señala las dimensiones máximas permitidas a un vehículo para el paso por un túnel o un puente) y maniobrabilidad. Para la explicación de esta tesis se han elegido tres casos para las simulaciones uno de ellos consta parte del proyecto ejecutado del Plan Binacional de Desarrollo de la Región Fronteriza Perú – Ecuador cuya ejecución fue prevista en un periodo de 12 años (2000-2009). Entre estos destacan cinco ejes viales binacionales que tienen por objetivos conformar una red de conexión de la zona fronteriza común en este caso se ha elegido el mejoramiento y Rehabilitación de la carretera Sullana – El Alamor eje vial N°02 de la interconexión vial Perú – Ecuador. El siguiente caso se refiere al Ovalo Gutiérrez también conocido como rotonda, glorieta o redondela es una intersección de carreteras (rutas), avenidas o calles que se confluyen están conectadas entre sí mediante un anillo. Estas minimizan el riesgo de accidente ya que fuerzan a reducir la velocidad El tercer caso se refiere a las intersecciones urbanas ofrecen un potencial de investigación en diseño geométrico de vías. En los últimos años, la demanda vial ha crecido por el aumento del número de vehículos automotores, se puede decir que la oferta es bastante inferior a la demanda de tránsito. Esto ha traído como consecuencia incrementos en la congestión, demoras, accidentes y problemas ambientales, bastante mayores que los considerados aceptables. Contamos con la capacidad, la topografía, los conocimientos, las condiciones de mejorar la vida de la malla vial y también con la necesidad de disponer de un instrumento idóneo para afrontar la solución de la actual problemática. Finalmente como aporte de la tesis verificaremos si los sobreanchos según la norma peruana. Si bien es cierto que la velocidad de diseño que es la máx. velocidad que se puede tomar para permitir garantizar seguridad y comodidad el cual va directamente relacionado con el cálculo y elección del radio mínimo utilizando la tabla 302.02 de la DG-2013.(Ministerio de Transportes y. , Agosto, 2014)

In this thesis several non-causal offline algorithms are developed and evaluated for a vision system used for pedestrian and vehicle traffic. The reason was to investigate if the performance increase of non-causal offline algorithms alone is enough to evaluate the performance of vision system. In recent years the vision systems have become one of the most important sensors for modern vehicles active security systems. The active security systems are becoming more important today and for them to work a good object detection and tracking in the vicinity of the vehicle is needed. Thus, the vision system needs to be properly evaluated. The problem is that modern evaluation techniques are limited to a few object scenarios and thus a more versatile evaluation technique is desired for the vision system. The focus of this thesis is to research non-causal offline techniques that increases the tracking performance without increasing the number of sensors. The Unscented Kalman Filter is used for state estimation and an unscented Rauch-Tung-Striebel smoother is used to propagate information backwards in time. Different motion models such as a constant velocity and coordinated turn are evaluated. Further assumptions and techniques such as tracking vehicles using fix width and estimating topography and using it as a measurement are evaluated. Evaluation shows that errors in velocity and the uncertainty of all the states are significantly reduced using an unscented Rauch-Tung-Striebel smoother. For the evaluated scenarios it can be concluded that the choice of motion model depends on scenarios and the motion of the tracked vehicle but are roughly the same. Further the results show that assuming fix width of a vehicle do not work and measurements using non-causal estimation of topography can significantly reduce the error in position, but further studies are recommended to verify this.