Dans la vie de tous les jours, nous assistons à des chorégraphies surprenantes dans les déplacements de foules de piétons. Les mécanismes qui sont à la base de la dynamique des foules humaines restent peu connus. Un des modes d’observation des piétons consiste à réaliser des mesures en conditions réelles (exemple : aéroport, gare, etc.). La trajectoire empruntée, la vitesse et l’accélération sont les données de base pour une telle analyse. C’est dans ce contexte que se placent nos travaux qui combinent étroitement observations en milieu naturel et expérimentations contrôlées. Nous avons proposé un système pour le suivi de plusieurs piétons dans un environnement fermé, à l’aide d’un réseau de télémètres lasers à balayage. Nous avons fait avancer l’état de l’art sur quatre plans.Premièrement, nous avons introduit une méthode de fusion automatique des données, permettant de discriminer les objets statiques (murs, poteaux, etc.) et aussi d’augmenter le taux de détection.Deuxièmement, nous avons proposé une méthode de détection non paramétrique basée sur la modélisation de la marche. L’algorithme estime la position du piéton, que celui-ci soit immobile ou en mouvement.Finalement, notre suivi repose sur la méthode Rao-Blackwell Monte Carlo Association de Données, avec la particularité de suivre un nombre variable de piétons.L’algorithme a été évalué quantitativement par des expériences de comportement social à différents niveaux de densité. Ces expériences ont eu lieu dans une école, près de 300 piétons ont été suivis dont une trentaine simultanément. / In everyday life, we witness surprising choreographies in the movements of crowds of pedestrians. The mechanisms that underlie the dynamics of human crowd dynamics remain poorly understood. One of the ways of observing pedestrians consists in taking measurements in real conditions (e. g. airport, station, etc.). The trajectory, speed and acceleration are the basic data for such an analysis. It is in this context that our work is placed, which closely combines observations in the natural environment with controlled experiments. We proposed a system for tracking multiple pedestrians in a closed environment using a network of scanning laser rangefinders. We have advanced the state of the art on four levels: first, we have introduced an automatic data fusion method to discriminate static objects (walls, poles, etc.) and also to increase the detection rate; second, we have proposed a non-parametric detection method based on walking modeling. The algorithm estimates the position of the pedestrian, whether stationary or moving, and finally, our monitoring is based on the Rao-Blackwell Monte Carlo Association Data Method, with the particularity of tracking a variable number of pedestrians, which was quantitatively evaluated by experiments in social behaviour at different levels of density. These experiments took place in a school, nearly 300 pedestrians were followed, about thirty of them simultaneously.

In this thesis study, single target tracking algorithms including IMM-PDA and IMM-IPDA algorithms / Optimal approaches in multitarget tracking including IMM-JPDA, IMM-IJPDA and IMM-JIPDA algorithms and an example of Linear Multi-target approaches in multitarget tracking including IMM-LMIPDA algorithm have been studied and implemented in MATLAB for comparison. Simulations were carried out in various realistic test scenarios including single target tracking, tracking of multiple targets moving in convoy fashion, two targets merging in a junction, two targets merging-departing in junctions and multitarget tracking under isolated tracks situations. RMSE performance, track loss and computational load evaluations were done for these algorithms under the test scenarios dealing with these situations. Benchmarkings are presented relying on these outcomes.

A cruise missile is a guided missile that uses a lifting wing and a jet propulsion system to allow sustained flight. Cruise missiles are, in essence, unmanned aircraft and they are generally designed to carry a large conventional or nuclear warhead many hundreds of miles with excellent accuracy. In this study, navigation and control studies on cruise missiles are performed. Due to the variety and complexity of the subsystems of the cruise missiles, the main concern is limited with the navigation system. Navigation system determines the position, velocity, attitude and time solutions of the missile. Therefore, it can be concluded that an accurate self-contained navigation system directly influences the success of the missile. In the study, modern radar data association algorithms are implemented as new Terrain Aided Navigation (TAN) algorithms which can be used with low-cost Inertial Measurement Units (IMU&rsquo / s). In order to perform the study, first a thorough survey of the literature on mid-course navigation of cruise missiles is performed. Then, study on modern radar data association algorithms and their implementations to TAN are done with simple simulations. At the case study part, a six degree of freedom (6 DOF) flight simulation tool is developed which includes the aerodynamic and dynamic model of the cruise missile model including error model of the navigation system. Finally, the performances of the designed navigation systems with the implemented TAN algorithms are examined in detail with the help of the simulations performed.

An analysis and simulation tool is developed for optimizing system parameters of the monopulse target tracking radar and observing effects of the system parameters on the performance of the system over different scenarios. A monopulse tracking radar is modeled for measuring the performance of the radar with given parameters, during the thesis studies. The radar model simulates the operation of a Class IA type monopulse automatic tracking radar, which uses a planar phased array. The interacting multiple model (IMM) estimator with the Probabilistic Data Association (PDA) technique is used as the tracking filter. In addition to modeling of the tracking radar model, an optimization tool is developed to optimize system parameters of this tracking radar model. The optimization tool implements a Genetic Algorithm (GA) belonging to a GA Toolbox distributed by Department of Automatic Control and System Engineering at University of Sheffield. The thesis presents optimization results over some given optimization scenarios and concludes on effect of tracking filter parameters, beamwidth and dwell interval for the confirmed track.

Incremental smoothing and mapping (iSAM) is presented, a novel approach to the simultaneous localization and mapping (SLAM) problem. SLAM is the problem of estimating an observer's position from local measurements only, while creating a consistent map of the environment. The problem is difficult because even very small errors in the local measurements accumulate over time and lead to large global errors. iSAM provides an exact and efficient solution to the SLAM estimation problem while also addressing data association. For the estimation problem, iSAM provides an exact solution by performing smoothing, which keeps all previous poses as part of the estimation problem, and therefore avoids linearization errors. iSAM uses methods from sparse linear algebra to provide an efficient incremental solution. In particular, iSAM deploys a direct equation solver based on QR matrix factorization of the naturally sparse smoothing information matrix. Instead of refactoring the matrix whenever new measurements arrive, only the entries of the factor matrix that actually change are calculated. iSAM is efficient even for robot trajectories with many loops as it performs periodic variable reordering to avoid unnecessary fill-in in the factor matrix. For the data association problem, I present state of the art data association techniques in the context of iSAM and present an efficient algorithm to obtain the necessary estimation uncertainties in real-time based on the factored information matrix. I systematically evaluate the components of iSAM as well as the overall algorithm using various simulated and real-world data sets.

Cette thèse présente l'utilisation conjointe de la théorie de l'évidente et du suivi multi-hypothèses pour la modélisation et la gestion d'un système de suivi multi-caméras dans un environnement autoroutier. Le suivi est basé sur la ré-identification des objets (véhicules) sur la base d'information visio-temporelles. Une concrétisation de ces concepts se traduit par la conception et la mise en oeuvre d'une architecture logicielle multi-agents de gestion du suivi multi-caméras. Après une présentation de l'état de l'art sur les cadres de gestion de l'incertain et celui relatif à fusion de l'information pour la mise en correspondance, et sur les systèmes multi-agents, notre apport dans ce travail se situe à trois niveaux. Le premier a été une adaptation de la phase de décision du modèle de croyance transférable pour y intégrer l'utilisation du suivi multi-hypothèses comme outil de levée d'ambigüité rn cas d'indécision face à une situation de mise en correspondance. Le second apport a été celui de proposer une architecture logicielle à base d'agents pour la gestion du système du suivi multi-caméras. Nous en avons proposé la modélisation globale ainsi que celle des agents et de leurs interactions en utilisant une démarche personnelle d'analyse mais toutefois inspirée de langages et outils de modélisation tels que Agent UML et MaSE pour ne citer que ceux-là, du fait qu'il n'existe pas réellement un standard normalisé à ce jour dans ce domaine. Notre troisième apport a été de faire un début d'implémentation de notre architecture logicielle à base d'agent en nous basant sur la plateforme JADE (Java Agent DEvelopment Framework). Quelques expérimentations et discussions des résultats sont présentées à la fin pour déboucher sur nos conclusions et perspectives. / This thesis presents the joint use of the theory of evidence and multiple hypothesis tracking for modeling and managing a system for monitoring multiple cameras in a motorway. The tracking is based on the re-identification of objects (vehicles) on the basis of visuals and times informations. A realization of these concepts results in the design and implementation of a software architecture for multiple agents management of multiple camera tracking system. After presenting the state of the art on the frameworks of uncertainty management and that on information fusion for the matching, and the multi-agent systems, our contribution in this work is on two or three levels. The first was an adaptation of the decision phase of the transferable belief model to incorporate the use of multi-hypotheses tracking as a tool of ambiguity survey in case of indecision in matching situation. The second contribution was a proposition of agent-based software architecture for management of a multiple cameras tracking system. We have proposed the global system modeling as well as agents and their interactions modeling using a personal analysis method but nevertheless inspired by modelisation languages and tolls such as Agent UML, MaSE and others, because there is not yet a standard and normalized tool on the subject. Our third contribution was to begin an implementation of our agent-based software architecture using JADE (Java Agent Development Framework). Some experiment and discussions are presented at the end to lead to our conclusions and perspectives.

Tato diplomova prace se zameruje na sledovani pohybu vice objektu. Prace popisuje dve implementace filtru, ktere jsou v podstate zalozeny na principu Kalmanova filtru. Obe implementace jsou zalozeny na principu sledovani vice objektu, na zaklade znalosti pozic vsech objektu v kazdem snimku. Prvni implementace je smisena verze Globalniho a Standardniho filtru nejblizsich sousedu. Druha implementace je postavena na pravde- podobnostnim pristupu k procesu sdruzeni. Posledni kapitola poskytuje srovnani mezi temito filtry a Zakladnim filtrem. Algoritmy byly realizovany v jave.

Multiple target tracking (MTT) is the process of localizing targets in an environment using sensors that perceive the environment. MTT has many applications such as wildlife monitoring, air traffic monitoring, and surveillance. These applications motivate further research in the different challenging aspects of MTT. One of these challenges that we will focus on in this dissertation is constructing a high fidelity target model. A common approach to target modeling is to use linear models or other simplified models that do not properly describe the target's pose (position and orientation), motion, and uncertainty. These simplified models are typically used because they are easy to implement and computationally efficient. A more accurate approach that improves tracking performance is to define the target model using a geometric representation of the target's natural configuration manifold. In essence, this geometric approach seeks to define a target model that can express every pose and motion of the target while preserving geometric properties such as distances and angles. We restrict our discussion of MTT to objects that move in physical space and can be modeled as a rigid body. This restriction allows us to construct generic geometric target models defined on Lie groups. Since not every Lie group has additional structure that permits vector space arithmetic like Euclidean space, many components of MTT such as data association, track initialization, track propagation and updating, track association and fusing, etc, must be adapted to work with Lie groups. The main contribution of this dissertation is the presentation of a novel MTT algorithm that implements the different MTT components to work with target models defined on Lie groups. We call this new algorithm, Geometric Multiple Target Tracking (G-MTT). This dissertation also serves as a guide on how other MTT algorithms can be modified to work with geometric target models. As part of the presentation there are various experimental results that strengthen the argument that a geometric approach to target modeling improves tracking performance.

No description available.

This thesis focuses on visual multiple-target tracking (MTT) from a UAV. Typical state-of-the-art multiple-target trackers rely on an object detector as the primary detection source. However, object detectors usually require a GPU to process images in real-time, which may not be feasible to carry on-board a UAV. Additionally, they often do not produce consistent detections for small objects typical of UAV imagery.In our method, we instead detect motion to identify objects of interest in the scene. We detect motion at corners in the image using optical flow. We also track points long-term to continue tracking stopped objects. Since our motion detection algorithm generates multiple detections at each time-step, we use a hybrid probabilistic data association filter combined with a single iteration of expectation maximization to improve tracking accuracy.We also present a motion detection algorithm that accounts for parallax in non-planar UAV imagery. We use the essential matrix to distinguish between true object motion and apparent object motion due to parallax. Instead of calculating the essential matrix directly, which can be time-consuming, we design a new algorithm that optimizes the rotation and translation between frames. This new algorithm requires only 4 ms instead of 47 ms per frame of the video sequence.We demonstrate the performance of these algorithms on video data. These algorithms are shown to improve tracking accuracy, reliability, and speed. All these contributions are capable of running in real-time without a GPU.