Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis

Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon establishing a non-null total effect of the exposure, investigators routinely wish to make inferences about the direct (indirect) pathway of the effect of the exposure not through (through) a mediator variable that occurs subsequently to the exposure and prior to the outcome. Although powerful semiparametric methodologies have been developed to analyze observational studies, that produce double robust and highly efficient estimates of the marginal total causal effect, similar methods for mediation analysis are currently lacking. Thus, this paper develops a general semiparametric framework for obtaining inferences about so-called marginal natural direct and indirect causal effects, while appropriately accounting for a large number of pre-exposure confounding factors for the exposure and the mediator variables. Our analytic framework is particularly appealing, because it gives new insights on issues of efficiency and robustness in the context of mediation analysis. In particular, we propose new multiply robust locally efficient estimators of the marginal natural indirect and direct causal effects, and develop a novel double robust sensitivity analysis framework for the assumption of ignorability of the mediator variable.

Place for video games : a theoretical and pedagogical framework for multiliteracies learning in English studies

Students are now involved in a vastly different textual landscape than many English scholars, one that relies on the “reading” and interpretation of multiple channels of simultaneous information. As a response to these new kinds of literate practices, my dissertation adds to the growing body of research on multimodal literacies, narratology in new media, and rhetoric through an examination of the place of video games in English teaching and research. I describe in this dissertation a hybridized theoretical basis for incorporating video games in English classrooms. This framework for textual analysis includes elements from narrative theory in literary study, rhetorical theory, and literacy theory, and when combined to account for the multiple modalities and complexities of gaming, can provide new insights about those theories and practices across all kinds of media, whether in written texts, films, or video games. In creating this framework, I hope to encourage students to view texts from a meta-level perspective, encompassing textual construction, use, and interpretation. In order to foster meta-level learning in an English course, I use specific theoretical frameworks from the fields of literary studies, narratology, film theory, aural theory, reader-response criticism, game studies, and multiliteracies theory to analyze a particular video game: World of Goo. These theoretical frameworks inform pedagogical practices used in the classroom for textual analysis of multiple media. Examining a video game from these perspectives, I use analytical methods from each, including close reading, explication, textual analysis, and individual elements of multiliteracies theory and pedagogy. In undertaking an in-depth analysis of World of Goo, I demonstrate the possibilities for classroom instruction with a complex blend of theories and pedagogies in English courses. This blend of theories and practices is meant to foster literacy learning across media, helping students develop metaknowledge of their own literate practices in multiple modes. Finally, I outline a design for a multiliteracies course that would allow English scholars to use video games along with other texts to interrogate texts as systems of information. In doing so, students can hopefully view and transform systems in their own lives as audiences, citizens, and workers.

Monitoring Programmes, Multiple Stress Analysis and Decision Support for River Basin Management

The identification of plausible causes for water body status deterioration will be much easier if it can build on available, reliable, extensive and comprehensive biogeochemical monitoring data (preferably aggregated in a database). A plausible identification of such causes is a prerequisite for well-informed decisions on which mitigation or remediation measures to take. In this chapter, first a rationale for an extended monitoring programme is provided; it is then compared to the one required by the Water Framework Directive (WFD). This proposal includes a list of relevant parameters that are needed for an integrated, a priori status assessment. Secondly, a few sophisticated statistical tools are described that subsequently allow for the estiation of the magnitude of impairment as well as the likely relative importance of different stressors in a multiple stressed environment. The advantages and restrictions of these rather complicated analytical methods are discussed. Finally, the use of Decision Support Systems (DSS) is advocated with regard to the specific WFD implementation requirements.

All-ceramic or metal-ceramic tooth-supported fixed dental prostheses (FDPs)? A systematic review of the survival and complication rates. Part II: Multiple-unit FDPs.

OBJECTIVE To assess the 5-year survival of metal-ceramic and all-ceramic tooth-supported fixed dental prostheses (FDPs) and to describe the incidence of biological, technical and esthetic complications. METHODS Medline (PubMed), Embase and Cochrane Central Register of Controlled Trials (CENTRAL) searches (2006-2013) were performed for clinical studies focusing on tooth-supported FDPs with a mean follow-up of at least 3 years. This was complemented by an additional hand search and the inclusion of 10 studies from a previous systematic review [1]. Survival and complication rates were analyzed using robust Poisson's regression models to obtain summary estimates of 5-year proportions. RESULTS Forty studies reporting on 1796 metal-ceramic and 1110 all-ceramic FDPs fulfilled the inclusion criteria. Meta-analysis of the included studies indicated an estimated 5-year survival rate of metal-ceramic FDPs of 94.4% (95% CI: 91.2-96.5%). The estimated survival rate of reinforced glass ceramic FDPs was 89.1% (95% CI: 80.4-94.0%), the survival rate of glass-infiltrated alumina FDPs was 86.2% (95% CI: 69.3-94.2%) and the survival rate of densely sintered zirconia FDPs was 90.4% (95% CI: 84.8-94.0%) in 5 years of function. Even though the survival rate of all-ceramic FDPs was lower than for metal-ceramic FDPs, the differences did not reach statistical significance except for the glass-infiltrated alumina FDPs (p=0.05). A significantly higher incidence of caries in abutment teeth was observed for densely sintered zirconia FDPs compared to metal-ceramic FDPs. Significantly more framework fractures were reported for reinforced glass ceramic FDPs (8.0%) and glass-infiltrated alumina FDPs (12.9%) compared to metal-ceramic FDPs (0.6%) and densely sintered zirconia FDPs (1.9%) in 5 years in function. However, the incidence of ceramic fractures and loss of retention was significantly (p=0.018 and 0.028 respectively) higher for densely sintered zirconia FDPs compared to all other types of FDPs. CONCLUSIONS Survival rates of all types of all-ceramic FDPs were lower than those reported for metal-ceramic FDPs. The incidence of framework fractures was significantly higher for reinforced glass ceramic FDPs and infiltrated glass ceramic FDPs, and the incidence for ceramic fractures and loss of retention was significantly higher for densely sintered zirconia FDPs compared to metal-ceramic FDPs.

Associating optical measurements and estimating orbits of geocentric objects through population-based meta-heuristic methods

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both, the correct associations among the observations and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. The number S corresponds to the number of fences involved in the problem. Each fence consists of a set of observations where each observation belongs to a different object. The S ≥ 3 MTT problem is an NP-hard combinatorial optimization problem. There are two general ways to solve this. One way is to seek the optimum solution, this can be achieved by applying a branch-and- bound algorithm. When using these algorithms the problem has to be greatly simplified to keep the computational cost at a reasonable level. Another option is to approximate the solution by using meta-heuristic methods. These methods aim to efficiently explore the different possible combinations so that a reasonable result can be obtained with a reasonable computational effort. To this end several population-based meta-heuristic methods are implemented and tested on simulated optical measurements. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.

Causes, forms and consequences of the professionalization of international sport federa-tions in Switzerland: Conceptual framework and first results

Synopsis: Sport organisations are facing multiple challenges originating from an increasingly complex and dynamic environment in general, and from internal changes in particular. Our study seeks to reveal and analyse the causes for professionalization processes in international sport federations, the forms resulting from it, as well as related consequences. Abstract: AIM OF ABSTRACT/PAPER - RESEARCH QUESTION Sport organisations are facing multiple challenges originating from an increasingly complex and dynamic environment in general, and from internal changes in particular. In this context, professionalization seems to have been adopted by sport organisations as an appropriate strategy to respond to pressures such as becoming more “business-like”. The ongoing study seeks to reveal and analyse the internal and external causes for professionalization processes in international sport federations, the forms resulting from it (e.g. organisational, managerial, economic) as well as related consequences on objectives, values, governance methods, performance management or again rationalisation. THEORETICAL BACKGROUND/LITERATURE REVIEW Studies on sport as specific non-profit sector mainly focus on the prospect of the “professionalization of individuals” (Thibault, Slack & Hinings, 1991), often within sport clubs (Thiel, Meier & Cachay, 2006) and national sport federations (Seippel, 2002) or on organisational change (Griginov & Sandanski, 2008; Slack & Hinings, 1987, 1992; Slack, 1985, 2001), thus leaving broader analysis on governance, management and professionalization in sport organisations an unaccomplished task. In order to further current research on above-mentioned topics, our intention is to analyse causes, forms and consequences of professionalisation processes in international sport federations. The social theory of action (Coleman, 1986; Esser, 1993) has been defined as appropriate theoretical framework, deriving in the following a multi-level framework for the analysis of sport organisations (Nagel, 2007). In light of the multi-level framework, sport federations are conceptualised as corporative actors whose objectives are defined and implemented with regard to the interests of member organisations (Heinemann, 2004) and/or other pressure groups. In order to understand social acting and social structures (Giddens 1984) of sport federations, two levels are in the focus of our analysis: the macro level examining the environment at large (political, social, economic systems etc.) and the meso level (Esser, 1999) examining organisational structures, actions and decisions of the federation’s headquarter as well as member organisations. METHODOLOGY, RESEARCH DESIGN AND DATA ANALYSIS The multi-level framework mentioned seeks to gather and analyse information on causes, forms and consequences of professionalization processes in sport federations. It is applied in a twofold approach: first an exploratory study based on nine semi-structured interviews with experts from umbrella sport organisations (IOC, WADA, ASOIF, AIOWF, etc.) as well as the analysis of related documents, relevant reports (IOC report 2000 on governance reform, Agenda 2020, etc.) and important moments of change in the Olympic Movement (Olympic revenue share, IOC evaluation criteria, etc.); and secondly several case studies. Whereas the exploratory study seeks more the causes for professionalization on an external, internal and headquarter level as depicted in the literature, the case studies rather focus on forms and consequences. Applying our conceptual framework, the analysis of forms is built around three dimensions: 1) Individuals (persons and positions), 2) Processes, structures (formalisation, specialisation), 3) Activities (strategic planning). With regard to consequences, we centre our attention on expectations of and relationships with stakeholders (e.g. cooperation with business partners), structure, culture and processes (e.g. governance models, performance), and expectations of and relationships with member organisations (e.g. centralisation vs. regionalisation). For the case studies, a mixed-method approach is applied to collect relevant data: questionnaires for rather quantitative data, interviews for rather qualitative data, as well as document and observatory analysis. RESULTS, DISCUSSION AND IMPLICATIONS/CONCLUSIONS With regard to causes of professionalization processes, we analyse the content of three different levels: 1. the external level, where the main pressure derives from financial resources (stakeholders, benefactors) and important turning points (scandals, media pressure, IOC requirements for Olympic sports); 2. the internal level, where pressure from member organisations turned out to be less decisive than assumed (little involvement of member organisations in decision-making); 3. the headquarter level, where specific economic models (World Cups, other international circuits, World Championships), and organisational structures (decision-making procedures, values, leadership) trigger or hinder a federation’s professionalization process. Based on our first analysis, an outline for an economic model is suggested, distinguishing four categories of IFs: “money-generating IFs” being rather based on commercialisation and strategic alliances; “classical Olympic IFs” being rather reactive and dependent on Olympic revenue; “classical non-Olympic IFs” being rather independent of the Olympic Movement; and “money-receiving IFs” being dependent on benefactors and having strong traditions and values. The results regarding forms and consequences will be outlined in the presentation. The first results from the two pilot studies will allow us to refine our conceptual framework for subsequent case studies, thus extending our data collection and developing fundamental conclusions. References: Bayle, E., & Robinson, L. (2007). A framework for understanding the performance of national governing bodies of sport. European Sport Management Quarterly, 7, 249–268 Chantelat, P. (2001). La professionnalisation des organisations sportives: Nouveaux débats, nouveaux enjeux [Professionalisation of sport organisations]. Paris: L’Harmattan. Dowling, M., Edwards, J., & Washington, M. (2014). Understanding the concept of professionalization in sport management research. Sport Management Review. Advance online publication. doi: 10.1016/j.smr.2014.02.003 Ferkins, L. & Shilbury, D. (2012). Good Boards Are Strategic: What Does That Mean for Sport Governance? Journal of Sport Management, 26, 67-80. Thibault, L., Slack, T., & Hinings, B. (1991). Professionalism, structures and systems: The impact of professional staff on voluntary sport organizations. International Review for the Sociology of Sport, 26, 83–97.

Comparison of multiple comparison methods for identifying differential gene expression in simulated and real papillary thyroid cancer microarray data

The difficulty of detecting differential gene expression in microarray data has existed for many years. Several correction procedures try to avoid the family-wise error rate in multiple comparison process, including the Bonferroni and Sidak single-step p-value adjustments, Holm's step-down correction method, and Benjamini and Hochberg's false discovery rate (FDR) correction procedure. Each multiple comparison technique has its advantages and weaknesses. We studied each multiple comparison method through numerical studies (simulations) and applied the methods to the real exploratory DNA microarray data, which detect of molecular signatures in papillary thyroid cancer (PTC) patients. According to our results of simulation studies, Benjamini and Hochberg step-up FDR controlling procedure is the best process among these multiple comparison methods and we discovered 1277 potential biomarkers among 54675 probe sets after applying the Benjamini and Hochberg's method to PTC microarray data.^

Statistical Methods for Differential Expressions of Genes Detected in Multiple-Condition Experiment of Microarray

Most studies of differential gene-expressions have been conducted between two given conditions. The two-condition experimental (TCE) approach is simple in that all genes detected display a common differential expression pattern responsive to a common two-condition difference. Therefore, the genes that are differentially expressed under the other conditions other than the given two conditions are undetectable with the TCE approach. In order to address the problem, we propose a new approach called multiple-condition experiment (MCE) without replication and develop corresponding statistical methods including inference of pairs of conditions for genes, new t-statistics, and a generalized multiple-testing method for any multiple-testing procedure via a control parameter C. We applied these statistical methods to analyze our real MCE data from breast cancer cell lines and found that 85 percent of gene-expression variations were caused by genotypic effects and genotype-ANAX1 overexpression interactions, which agrees well with our expected results. We also applied our methods to the adenoma dataset of Notterman et al. and identified 93 differentially expressed genes that could not be found in TCE. The MCE approach is a conceptual breakthrough in many aspects: (a) many conditions of interests can be conducted simultaneously; (b) study of association between differential expressions of genes and conditions becomes easy; (c) it can provide more precise information for molecular classification and diagnosis of tumors; (d) it can save lot of experimental resources and time for investigators.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Space-time Reconstruction of Oceanic Sea States via Variational Stereo Methods

We present a remote sensing observational method for the measurement of the spatio-temporal dynamics of ocean waves. Variational techniques are used to recover a coherent space-time reconstruction of oceanic sea states given stereo video imagery. The stereoscopic reconstruction problem is expressed in a variational optimization framework. There, we design an energy functional whose minimizer is the desired temporal sequence of wave heights. The functional combines photometric observations as well as spatial and temporal regularizers. A nested iterative scheme is devised to numerically solve, via 3-D multigrid methods, the system of partial differential equations resulting from the optimality condition of the energy functional. The output of our method is the coherent, simultaneous estimation of the wave surface height and radiance at multiple snapshots. We demonstrate our algorithm on real data collected off-shore. Statistical and spectral analysis are performed. Comparison with respect to an existing sequential method is analyzed.

Video analysis based vehicle detection and tracking using an MCMC sampling framework

This article presents a probabilistic method for vehicle detection and tracking through the analysis of monocular images obtained from a vehicle-mounted camera. The method is designed to address the main shortcomings of traditional particle filtering approaches, namely Bayesian methods based on importance sampling, for use in traffic environments. These methods do not scale well when the dimensionality of the feature space grows, which creates significant limitations when tracking multiple objects. Alternatively, the proposed method is based on a Markov chain Monte Carlo (MCMC) approach, which allows efficient sampling of the feature space. The method involves important contributions in both the motion and the observation models of the tracker. Indeed, as opposed to particle filter-based tracking methods in the literature, which typically resort to observation models based on appearance or template matching, in this study a likelihood model that combines appearance analysis with information from motion parallax is introduced. Regarding the motion model, a new interaction treatment is defined based on Markov random fields (MRF) that allows for the handling of possible inter-dependencies in vehicle trajectories. As for vehicle detection, the method relies on a supervised classification stage using support vector machines (SVM). The contribution in this field is twofold. First, a new descriptor based on the analysis of gradient orientations in concentric rectangles is dened. This descriptor involves a much smaller feature space compared to traditional descriptors, which are too costly for real-time applications. Second, a new vehicle image database is generated to train the SVM and made public. The proposed vehicle detection and tracking method is proven to outperform existing methods and to successfully handle challenging situations in the test sequences.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

An Efficient Multiple Object Detection and Tracking Framework for Automatic Counting and Video Surveillance Applications

Automatic visual object counting and video surveillance have important applications for home and business environments, such as security and management of access points. However, in order to obtain a satisfactory performance these technologies need professional and expensive hardware, complex installations and setups, and the supervision of qualified workers. In this paper, an efficient visual detection and tracking framework is proposed for the tasks of object counting and surveillance, which meets the requirements of the consumer electronics: off-the-shelf equipment, easy installation and configuration, and unsupervised working conditions. This is accomplished by a novel Bayesian tracking model that can manage multimodal distributions without explicitly computing the association between tracked objects and detections. In addition, it is robust to erroneous, distorted and missing detections. The proposed algorithm is compared with a recent work, also focused on consumer electronics, proving its superior performance.