A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

Contribution to the improvement of communication services in nanosatellite constellations using delay and disruption tolerant networking based architectures

Esta tesis se desarrolla dentro del marco de las comunicaciones satelitales en el innovador campo de los pequeños satélites también llamados nanosatélites o cubesats, llamados así por su forma cubica. Estos nanosatélites se caracterizan por su bajo costo debido a que usan componentes comerciales llamados COTS (commercial off-the-shelf) y su pequeño tamaño como los Cubesats 1U (10cm*10 cm*10 cm) con masa aproximada a 1 kg. Este trabajo de tesis tiene como base una iniciativa propuesta por el autor de la tesis para poner en órbita el primer satélite peruano en mi país llamado chasqui I, actualmente puesto en órbita desde la Estación Espacial Internacional. La experiencia de este trabajo de investigación me llevo a proponer una constelación de pequeños satélites llamada Waposat para dar servicio de monitoreo de sensores de calidad de agua a nivel global, escenario que es usado en esta tesis. Es ente entorno y dadas las características limitadas de los pequeños satélites, tanto en potencia como en velocidad de datos, es que propongo investigar una nueva arquitectura de comunicaciones que permita resolver en forma óptima la problemática planteada por los nanosatélites en órbita LEO debido a su carácter disruptivo en sus comunicaciones poniendo énfasis en las capas de enlace y aplicación. Esta tesis presenta y evalúa una nueva arquitectura de comunicaciones para proveer servicio a una red de sensores terrestres usando una solución basada en DTN (Delay/Disruption Tolerant Networking) para comunicaciones espaciales. Adicionalmente, propongo un nuevo protocolo de acceso múltiple que usa una extensión del protocolo ALOHA no ranurado, el cual toma en cuenta la prioridad del trafico del Gateway (ALOHAGP) con un mecanismo de contienda adaptativo. Utiliza la realimentación del satélite para implementar el control de la congestión y adapta dinámicamente el rendimiento efectivo del canal de una manera óptima. Asumimos un modelo de población de sensores finito y una condición de tráfico saturado en el que cada sensor tiene siempre tramas que transmitir. El desempeño de la red se evaluó en términos de rendimiento efectivo, retardo y la equidad del sistema. Además, se ha definido una capa de convergencia DTN (ALOHAGP-CL) como un subconjunto del estándar TCP-CL (Transmission Control Protocol-Convergency Layer). Esta tesis muestra que ALOHAGP/CL soporta adecuadamente el escenario DTN propuesto, sobre todo cuando se utiliza la fragmentación reactiva. Finalmente, esta tesis investiga una transferencia óptima de mensajes DTN (Bundles) utilizando estrategias de fragmentación proactivas para dar servicio a una red de sensores terrestres utilizando un enlace de comunicaciones satelitales que utiliza el mecanismo de acceso múltiple con prioridad en el tráfico de enlace descendente (ALOHAGP). El rendimiento efectivo ha sido optimizado mediante la adaptación de los parámetros del protocolo como una función del número actual de los sensores activos recibidos desde el satélite. También, actualmente no existe un método para advertir o negociar el tamaño máximo de un “bundle” que puede ser aceptado por un agente DTN “bundle” en las comunicaciones por satélite tanto para el almacenamiento y la entrega, por lo que los “bundles” que son demasiado grandes son eliminados o demasiado pequeños son ineficientes. He caracterizado este tipo de escenario obteniendo una distribución de probabilidad de la llegada de tramas al nanosatélite así como una distribución de probabilidad del tiempo de visibilidad del nanosatélite, los cuales proveen una fragmentación proactiva óptima de los DTN “bundles”. He encontrado que el rendimiento efectivo (goodput) de la fragmentación proactiva alcanza un valor ligeramente inferior al de la fragmentación reactiva. Esta contribución permite utilizar la fragmentación activa de forma óptima con todas sus ventajas tales como permitir implantar el modelo de seguridad de DTN y la simplicidad al implementarlo en equipos con muchas limitaciones de CPU y memoria. La implementación de estas contribuciones se han contemplado inicialmente como parte de la carga útil del nanosatélite QBito, que forma parte de la constelación de 50 nanosatélites que se está llevando a cabo dentro del proyecto QB50. ABSTRACT This thesis is developed within the framework of satellite communications in the innovative field of small satellites also known as nanosatellites (<10 kg) or CubeSats, so called from their cubic form. These nanosatellites are characterized by their low cost because they use commercial components called COTS (commercial off-the-shelf), and their small size and mass, such as 1U Cubesats (10cm * 10cm * 10cm) with approximately 1 kg mass. This thesis is based on a proposal made by the author of the thesis to put into orbit the first Peruvian satellite in his country called Chasqui I, which was successfully launched into orbit from the International Space Station in 2014. The experience of this research work led me to propose a constellation of small satellites named Waposat to provide water quality monitoring sensors worldwide, scenario that is used in this thesis. In this scenario and given the limited features of nanosatellites, both power and data rate, I propose to investigate a new communications architecture that allows solving in an optimal manner the problems of nanosatellites in orbit LEO due to the disruptive nature of their communications by putting emphasis on the link and application layers. This thesis presents and evaluates a new communications architecture to provide services to terrestrial sensor networks using a space Delay/Disruption Tolerant Networking (DTN) based solution. In addition, I propose a new multiple access mechanism protocol based on extended unslotted ALOHA that takes into account the priority of gateway traffic, which we call ALOHA multiple access with gateway priority (ALOHAGP) with an adaptive contention mechanism. It uses satellite feedback to implement the congestion control, and to dynamically adapt the channel effective throughput in an optimal way. We assume a finite sensor population model and a saturated traffic condition where every sensor always has frames to transmit. The performance was evaluated in terms of effective throughput, delay and system fairness. In addition, a DTN convergence layer (ALOHAGP-CL) has been defined as a subset of the standard TCP-CL (Transmission Control Protocol-Convergence Layer). This thesis reveals that ALOHAGP/CL adequately supports the proposed DTN scenario, mainly when reactive fragmentation is used. Finally, this thesis investigates an optimal DTN message (bundles) transfer using proactive fragmentation strategies to give service to a ground sensor network using a nanosatellite communications link which uses a multi-access mechanism with priority in downlink traffic (ALOHAGP). The effective throughput has been optimized by adapting the protocol parameters as a function of the current number of active sensors received from satellite. Also, there is currently no method for advertising or negotiating the maximum size of a bundle which can be accepted by a bundle agent in satellite communications for storage and delivery, so that bundles which are too large can be dropped or which are too small are inefficient. We have characterized this kind of scenario obtaining a probability distribution for frame arrivals to nanosatellite and visibility time distribution that provide an optimal proactive fragmentation of DTN bundles. We have found that the proactive effective throughput (goodput) reaches a value slightly lower than reactive fragmentation approach. This contribution allows to use the proactive fragmentation optimally with all its advantages such as the incorporation of the security model of DTN and simplicity in protocol implementation for computers with many CPU and memory limitations. The implementation of these contributions was initially contemplated as part of the payload of the nanosatellite QBito, which is part of the constellation of 50 nanosatellites envisaged under the QB50 project.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Lightweight Internet Traffic Classification - A Subject Based Solution with Word Embeddings

Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application-level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word-embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $ 3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.

Traffic based energy consumption optimisation to improve the lifetime and performance of ad hoc wireless sensor networks

Ad hoc wireless sensor networks (WSNs) are formed from self-organising configurations of distributed, energy constrained, autonomous sensor nodes. The service lifetime of such sensor nodes depends on the power supply and the energy consumption, which is typically dominated by the communication subsystem. One of the key challenges in unlocking the potential of such data gathering sensor networks is conserving energy so as to maximize their post deployment active lifetime. This thesis described the research carried on the continual development of the novel energy efficient Optimised grids algorithm that increases the WSNs lifetime and improves on the QoS parameters yielding higher throughput, lower latency and jitter for next generation of WSNs. Based on the range and traffic relationship the novel Optimised grids algorithm provides a robust traffic dependent energy efficient grid size that minimises the cluster head energy consumption in each grid and balances the energy use throughout the network. Efficient spatial reusability allows the novel Optimised grids algorithm improves on network QoS parameters. The most important advantage of this model is that it can be applied to all one and two dimensional traffic scenarios where the traffic load may fluctuate due to sensor activities. During traffic fluctuations the novel Optimised grids algorithm can be used to re-optimise the wireless sensor network to bring further benefits in energy reduction and improvement in QoS parameters. As the idle energy becomes dominant at lower traffic loads, the new Sleep Optimised grids model incorporates the sleep energy and idle energy duty cycles that can be implemented to achieve further network lifetime gains in all wireless sensor network models. Another key advantage of the novel Optimised grids algorithm is that it can be implemented with existing energy saving protocols like GAF, LEACH, SMAC and TMAC to further enhance the network lifetimes and improve on QoS parameters. The novel Optimised grids algorithm does not interfere with these protocols, but creates an overlay to optimise the grids sizes and hence transmission range of wireless sensor nodes.

Linear Classifiers Based on Binary Distributed Representations

Binary distributed representations of vector data (numerical, textual, visual) are investigated in classification tasks. A comparative analysis of results for various methods and tasks using artificial and real-world data is given.

Multiclass Adaboost Based on an Ensemble of Binary Adaboosts

This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.

TRAFFIC IMPACT ANALYSIS OF SEVERAL DYNAMIC LANE MANAGEMENT STRATEGIES FOR CONGESTION MITIGATION BASED ON DTA MODEL

Persistent daily congestion has been increasing in recent years, particularly along major corridors during selected periods in the mornings and evenings. On certain segments, these roadways are often at or near capacity. However, a conventional Predefined control strategy did not fit the demands that changed over time, making it necessary to implement the various dynamical lane management strategies discussed in this thesis. Those strategies include hard shoulder running, reversible HOV lanes, dynamic tolls and variable speed limit. A mesoscopic agent-based DTA model is used to simulate different strategies and scenarios. From the analyses, all strategies aim to mitigate congestion in terms of the average speed and average density. The largest improvement can be found in hard shoulder running and reversible HOV lanes while the other two provide more stable traffic. In terms of average speed and travel time, hard shoulder running is the most congested strategy for I-270 to help relieve the traffic pressure.

A Multi-Criteria GIS-Based Route Selection Tool for Hazardous Material Transport: Consideration of Environmental Consequence, Traffic Congestions and Costs

Hazardous materials are substances that, if not regulated, can pose a threat to human populations and their environmental health, safety or property when transported in commerce. About 1.5 million tons of hazardous material shipments are transported by truck in the US annually, with a steady increase of approximately 5% per year. The objective of this study was to develop a routing tool for hazardous material transport in order to facilitate reduced environmental impacts and less transportation difficulties, yet would also find paths that were still compelling for the shipping carriers as a matter of trucking cost. The study started with identification of inhalation hazard impact zones and explosion protective areas around the location of hypothetical hazardous material releases, considering different parameters (i.e., chemicals characteristics, release quantities, atmospheric condition, etc.). Results showed that depending on the quantity of release, chemical, and atmospheric stability (a function of wind speed, meteorology, sky cover, time and location of accidents, etc.) the consequence of these incidents can differ. The study was extended by selection of other evaluation criteria for further investigation because health risk as an evaluation criterion would not be the only concern in selection of routes. Transportation difficulties (i.e., road blockage and congestion) were incorporated as important factor due to their indirect impact/cost on the users of transportation networks. Trucking costs were also considered as one of the primary criteria in selection of hazardous material paths; otherwise the suggested routes would have not been convincing for the shipping companies. The last but not least criterion was proximity of public places to the routes. The approach evolved from a simple framework to a complicated and efficient GIS-based tool able to investigate transportation networks of any given study area, and capable of generating best routing options for cargos. The suggested tool uses a multi-criteria-decision-making method, which considers the priorities of the decision makers in choosing the cargo routes. Comparison of the routing options based on each criterion and also the overall suitableness of the path in regards to all the criteria (using a multi-criteria-decision-making method) showed that using similar tools as the one proposed by this study can provide decision makers insights in the area of hazardous material transport. This tool shows the probable consequences of considering each path in a very easily understandable way; in the formats of maps and tables, which makes the tradeoffs of costs and risks considerably simpler, as in some cases slightly compromising on trucking cost may drastically decrease the probable health risk and/or traffic difficulties. This will not only be rewarding to the community by making cities safer places to live, but also can be beneficial to shipping companies by allowing them to advertise as environmental friendly conveyors.

Effects of truck traffic on top-down fatigue cracking performance of flexible pavements using a new mechanics-based analysis framework

The mechanics-based analysis framework predicts top-down fatigue cracking initiation time in asphalt concrete pavements by utilising fracture mechanics and mixture morphology-based property. To reduce the level of complexity involved, traffic data were characterised and incorporated into the framework using the equivalent single axle load (ESAL) approach. There is a concern that this kind of simplistic traffic characterisation might result in erroneous performance predictions and pavement structural designs. This paper integrates axle load spectra and other traffic characterisation parameters into the mechanics-based analysis framework and studies the impact these traffic characterisation parameters have on predicted fatigue cracking performance. The traffic characterisation inputs studied are traffic growth rate, axle load spectra, lateral wheel wander and volume adjustment factors. For this purpose, a traffic integration approach which incorporates Monte Carlo simulation and representative traffic characterisation inputs was developed. The significance of these traffic characterisation parameters was established by evaluating a number of field pavement sections. It is evident from the results that all the traffic characterisation parameters except truck wheel wander have been observed to have significant influence on predicted top-down fatigue cracking performance.

2D-3D Face Recognition Based on PCA and Feature Modelling

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.

Analysis of road potential and bottlenecks based on operating speed

Vehicle detectors have been installed at approximately every 300 meters on each lane on Tokyo metropolitan expressway. Various traffic data such as traffic volume, average speed and time occupancy are collected by vehicle detectors. We can understand traffic characteristics of every point by comparing traffic data collected at consecutive points. In this study, we focused on average speed, analyzed road potential by operating speed during free-flow conditions, and identified latent bottlenecks. Furthermore, we analyzed effects for road potential by the rainfall level and day of the week. It’s expected that this method of analysis will be utilized for installation of ITS such as drive assist, estimation of parameters for traffic simulation and feedback to road design as congestion measures.