Cette thèse s'inscrit dans le contexte de la vidéo surveillance et s'intéresse à la ré-identification de personnes dans un réseau de caméras à champs disjoints. La ré-identification consiste à déterminer si une personne quitte le champ d'une caméra et réapparait dans une autre. Elle est particulièrement difficile car l'apparence de la personne change de manière significative à cause de différents facteurs. Nous proposons d'exploiter la complémentarité de l'apparence de la personne et son style de mouvement pour la décrire d'une manière appropriée aux facteurs de complexité. C'est une nouvelle approche car la ré-identification a été traitée par des approches d'apparence. Les contributions majeures proposées concernent: la description de la personne et l'appariement des primitives. Nous étudions deux scénarios de ré-identification : simple et complexe. Dans le scénario simple, nous étudions la faisabilité de deux approches : approche biométrique basée sur la démarche et approche d'apparence fondée sur des points d'intérêt (PI) spatiaux et des primitives de couleur. Dans le scénario complexe, nous proposons de fusionner des primitives d'apparence et de mouvement. Nous décrivons le mouvement par des Pis spatio-temporels et l'apparence par des PIs spatiaux. Pour l'appariement, nous utilisons la représentation parcimonieuse comme méthode d'appariement local entre les PIs. Le schéma de fusion est fondé sur le calcul de la somme pondérée des votes des PIs et ensuite l'application de la règle de vote majoritaire. Nous proposons également une analyse d'erreurs permettant d'identifier les sources d'erreurs de notre système pour dégager les pistes d'amélioration les plus prometteuses / This thesis focuses on the problem of hu man re-identification through a network of cameras with non overlapping fields of view. Human re-identification is defined as the task of determining if a persan leaving the field of one camera reappears in another. It is particularly difficult because of persons' significant appearance change within different cameras vision fields due to various factors. In this work, we propose to exploit the complementarity of the person's appearance and style of movement that leads to a description that is more robust with respect to various complexity factors. This is a new approach for the re-identification problem that is usually treated by appearance methods only. The major contributions proposed in this work include: person's description and features matching. First we study the re-identification problem and classify it into two scenarios: simple and complex. In the simple scenario, we study the feasibility of two approaches: a biometric approach based on gait and an appearance approach based on spatial Interest Points (IPs) and color features. In the complex scenario, we propose to exploit a fusion strategy of two complementary features provided by appearance and motion descriptions. We describe motion using spatiotemporal IPs, and use the spatial IPs for describing the appearance. For feature matching, we use sparse representation as a local matching method between IPs. The fusion strategy is based on the weighted sum of matched IPs votes and then applying the rule of majority vote. Moreover, we have carried out an error analysis to identify the sources of errors in our proposed system to identify the most promising areas for improvement

La ré-identification de véhicules permet d’estimer deux paramètres clés en gestion dynamique de trafic : les temps de parcours et les matrices origine-destination. Dans cette thèse, nous avons choisi d'effectuer cette ré-identification par analyse des signatures magnétiques mesurées par des capteurs tri-axiaux placés sur la chaussée. La signature magnétique est générée par l'aimantation du véhicule : elle est alors susceptible de varier en fonction de l'orientation du véhicule par rapport au champ magnétique terrestre (à cause de l'aimantation induite dans le plan horizontal), et en fonction de sa position latérale relative par rapport aux capteurs. Les expérimentations que nous avons menées nous ont permis d'obtenir une base de données de signatures magnétiques, et ainsi d'évaluer les performances des deux méthodes de ré-identification que nous avons élaborées.La première méthode consiste à comparer directement des paires de signatures magnétiques mesurées par les capteurs. Les calculs de distances entre les paires sont effectués avec des algorithmes classiques comme la distance euclidienne. Les résultats obtenus sont très bons, et baissent peu lorsque le véhicule change d'orientation. Toutefois, ils sont très sensibles à la déformation des signaux due au décalage latéral du véhicule, et nécessitent donc de positionner un capteur tous les 0.20m sur toute la largeur de la voie.Dans un second temps, nous proposons une méthode de ré-identification qui compare des paires de modèles magnétiques de véhicules. Ces modèles sont composés de plusieurs dipôles, et sont calculés à partir des signatures mesurées. La modélisation a pour but de s’affranchir du décalage latéral du véhicule, en remontant à la position relative du véhicule par rapport aux capteurs. Avec deux fois moins de capteurs que la méthode précédente, les résultats obtenus sur signaux réels sont également très bons, même s'ils sont un peu plus sensibles au changement d'orientation du véhicule. De plus, une simulation nous permet d'extrapoler qu'il est effectivement possible de s'affranchir du décalage latéral avec cette méthode. / Vehicle re-identification gives access to two essential data for dynamic traffic management: travel times and origin-destination matrices. In this thesis, we chose to re-identify vehicles by analysing their magnetic signatures measured with several 3-axis magnetic sensors located on the road. A magnetic signature is created by the vehicle magnetization. Therefore, the vehicle orientation to the Earth’s magnetic field (which determines the induced magnetization) and the variation of the lateral position of the vehicle relative to the sensors’ one might both have an impact on the magnetic signature. We gathered our experiments’ results into a database of magnetic signatures that we used to evaluate the performances of the two vehicle re-identification methods we developed.The first method is a direct comparison of pairs of magnetic signatures measured by the sensors. Distances between pairs of signatures are computed using classic algorithms such as the Euclidean distance. This method’s results are very positive and the vehicle change of orientation has only a slight impact on them. However, the distortion of signals due to a lateral offset in the vehicle position has a strong impact on the results. As a consequence, sensors have to be placed every 0.20m over the road’s entire width.The second proposed method compares pairs of vehicles’ magnetic models. Those models are composed of several magnetic dipoles and are determined from the measured signatures. Magnetic modelling aims to suppress the influence of the vehicle lateral position on the results by assessing the relative position of the vehicle above the sensors. Although the vehicle orientation has slightly more impact on the performances than with the first method, the overall results are more promising. This method also allows us to divide by two the number of sensors used.

Dans cette thèse, nous nous sommes intéressés au problème de la ré-identification de personnes dans le cadre de bases d'identités ouvertes. Ré-identifier une personne suppose qu'elle a déjà été identifiée auparavant. La galerie fait référence aux identités connues. Dans le cas de bases d'identités ouvertes, la galerie ne contient pas toutes les identités possibles. Ainsi une personne requête peut être une des personnes de la galerie, mais peut aussi ne pas être présente dans la galerie. Ré-identifier en base ouverte consiste donc non seulement à ranger par ordre de similarité les identités galeries les plus semblables à la personne requête mais également à rejeter les personnes requêtes si elles ne correspondent à aucune personne de la galerie. Une de nos contributions, COPReV, s'appuie exclusivement sur des contraintes de vérification afin d'apprendre une projection des descripteurs telle que la distance entre les descripteurs d'une même personne soit inférieure à un seuil et que la distance entre les descripteurs de deux personnes distinctes soit supérieure au même seuil. Nos autres contributions se basent sur des méthodes parcimonieuses collaboratives qui sont performantes pour résoudre des tâches de classement. Nous proposons d'améliorer ces méthodes en introduisant un aspect vérification grâce à une collaboration élargie. De plus, une variante bidirectionnelle de cette approche la rend encore plus robuste et donne des résultats meilleurs que les autres approches actuelles de l'état de l'art dans le cadre de la ré-identification de personne en base d'identités ouverte. / In this thesis we tackle the open world person re-identification task in which the people we want to re-identify (probe) might not appear in the database of known identities (gallery). For a given probe person, the goal is to find out whether he is present in the gallery or not and if so, who he is. Our first contribution is based on a verification formulation of the problem. A linear transformation of the features is learnt so that the distance between features of the same person are below a threshold and that of distinct people are above that same threshold so that it is easy to determine whether two sets of images represent the same person or not. Our other contributions are based on collaborative sparse representations. A usual way to use collaborative sparse representation for re-identification is to approximate the feature of a probe image by a sparse linear combination of gallery elements, where all the known identities collaborate but only the most similar elements are selected. Gallery identities are then ranked according to how much they contributed to the approximation. We propose to enhance the collaborative aspect so that collaborative sparse representations can be used not only as a ranking tool but also as a detection tool which rejects wrong matches. A bidirectional variant gives even more robust results by taking into account the fact that a good match is a match where there is a reciprocal relation in which both the probe and the gallery identities consider the other one as a good match. COPReV shows average performances but bidirectional collaboration enhanced sparse representation method outperforms state-of-the-art methods for open world scenarios.

La ré-identification de véhicules permet d’estimer deux paramètres clés en gestion dynamique de trafic : les temps de parcours et les matrices origine-destination. Dans cette thèse, nous avons choisi d'effectuer cette ré-identification par analyse des signatures magnétiques mesurées par des capteurs tri-axiaux placés sur la chaussée. La signature magnétique est générée par l'aimantation du véhicule : elle est alors susceptible de varier en fonction de l'orientation du véhicule par rapport au champ magnétique terrestre (à cause de l'aimantation induite dans le plan horizontal), et en fonction de sa position latérale relative par rapport aux capteurs. Les expérimentations que nous avons menées nous ont permis d'obtenir une base de données de signatures magnétiques, et ainsi d'évaluer les performances des deux méthodes de ré-identification que nous avons élaborées.La première méthode consiste à comparer directement des paires de signatures magnétiques mesurées par les capteurs. Les calculs de distances entre les paires sont effectués avec des algorithmes classiques comme la distance euclidienne. Les résultats obtenus sont très bons, et baissent peu lorsque le véhicule change d'orientation. Toutefois, ils sont très sensibles à la déformation des signaux due au décalage latéral du véhicule, et nécessitent donc de positionner un capteur tous les 0.20m sur toute la largeur de la voie.Dans un second temps, nous proposons une méthode de ré-identification qui compare des paires de modèles magnétiques de véhicules. Ces modèles sont composés de plusieurs dipôles, et sont calculés à partir des signatures mesurées. La modélisation a pour but de s’affranchir du décalage latéral du véhicule, en remontant à la position relative du véhicule par rapport aux capteurs. Avec deux fois moins de capteurs que la méthode précédente, les résultats obtenus sur signaux réels sont également très bons, même s'ils sont un peu plus sensibles au changement d'orientation du véhicule. De plus, une simulation nous permet d'extrapoler qu'il est effectivement possible de s'affranchir du décalage latéral avec cette méthode. / Vehicle re-identification gives access to two essential data for dynamic traffic management: travel times and origin-destination matrices. In this thesis, we chose to re-identify vehicles by analysing their magnetic signatures measured with several 3-axis magnetic sensors located on the road. A magnetic signature is created by the vehicle magnetization. Therefore, the vehicle orientation to the Earth’s magnetic field (which determines the induced magnetization) and the variation of the lateral position of the vehicle relative to the sensors’ one might both have an impact on the magnetic signature. We gathered our experiments’ results into a database of magnetic signatures that we used to evaluate the performances of the two vehicle re-identification methods we developed.The first method is a direct comparison of pairs of magnetic signatures measured by the sensors. Distances between pairs of signatures are computed using classic algorithms such as the Euclidean distance. This method’s results are very positive and the vehicle change of orientation has only a slight impact on them. However, the distortion of signals due to a lateral offset in the vehicle position has a strong impact on the results. As a consequence, sensors have to be placed every 0.20m over the road’s entire width.The second proposed method compares pairs of vehicles’ magnetic models. Those models are composed of several magnetic dipoles and are determined from the measured signatures. Magnetic modelling aims to suppress the influence of the vehicle lateral position on the results by assessing the relative position of the vehicle above the sensors. Although the vehicle orientation has slightly more impact on the performances than with the first method, the overall results are more promising. This method also allows us to divide by two the number of sensors used.

[EN] The presence of surveillance systems in our lives has drastically increased during the last years. Camera networks can be seen in almost every crowded public and private place, which generate huge amount of data with valuable information. The automatic analysis of data plays an important role to extract relevant information from the scene. In particular, the problem of person re-identification is a prominent topic that has become of great interest, specially for the fields of security or marketing. However, there are some factors, such as changes in the illumination conditions, variations in the person pose, occlusions or the presence of outliers that make this topic really challenging. Fortunately, the recent introduction of new technologies such as depth cameras opens new paradigms in the image processing field and brings new possibilities. This Thesis proposes a new complete framework to tackle the problem of person re-identification using commercial rgb-depth cameras. This work includes the analysis and evaluation of new approaches for the modules of segmentation, tracking, description and matching. To evaluate our contributions, a public dataset for person re-identification using rgb-depth cameras has been created. Rgb-depth cameras provide accurate 3D point clouds with color information. Based on the analysis of the depth information, an novel algorithm for person segmentation is proposed and evaluated. This method accurately segments any person in the scene, and naturally copes with occlusions and connected people. The segmentation mask of a person generates a 3D person cloud, which can be easily tracked over time based on proximity. The accumulation of all the person point clouds over time generates a set of high dimensional color features, named raw features, that provides useful information about the person appearance. In this Thesis, we propose a family of methods to extract relevant information from the raw features in different ways. The first approach compacts the raw features into a single color vector, named Bodyprint, that provides a good generalisation of the person appearance over time. Second, we introduce the concept of 3D Bodyprint, which is an extension of the Bodyprint descriptor that includes the angular distribution of the color features. Third, we characterise the person appearance as a bag of color features that are independently generated over time. This descriptor receives the name of Bag of Appearances because its similarity with the concept of Bag of Words. Finally, we use different probabilistic latent variable models to reduce the feature vectors from a statistical perspective. The evaluation of the methods demonstrates that our proposals outperform the state of the art. / [ES] La presencia de sistemas de vigilancia se ha incrementado notablemente en los últimos anños. Las redes de videovigilancia pueden verse en casi cualquier espacio público y privado concurrido, lo cual genera una gran cantidad de datos de gran valor. El análisis automático de la información juega un papel importante a la hora de extraer información relevante de la escena. En concreto, la re-identificación de personas es un campo que ha alcanzado gran interés durante los últimos años, especialmente en seguridad y marketing. Sin embargo, existen ciertos factores, como variaciones en las condiciones de iluminación, variaciones en la pose de la persona, oclusiones o la presencia de artefactos que hacen de este campo un reto. Afortunadamente, la introducción de nuevas tecnologías como las cámaras de profundidad plantea nuevos paradigmas en la visión artificial y abre nuevas posibilidades. En esta Tesis se propone un marco completo para abordar el problema de re-identificación utilizando cámaras rgb-profundidad. Este trabajo incluye el análisis y evaluación de nuevos métodos de segmentación, seguimiento, descripción y emparejado de personas. Con el fin de evaluar las contribuciones, se ha creado una base de datos pública para re-identificación de personas usando estas cámaras. Las cámaras rgb-profundidad proporcionan nubes de puntos 3D con información de color. A partir de la información de profundidad, se propone y evalúa un nuevo algoritmo de segmentación de personas. Este método segmenta de forma precisa cualquier persona en la escena y resuelve de forma natural problemas de oclusiones y personas conectadas. La máscara de segmentación de una persona genera una nube de puntos 3D que puede ser fácilmente seguida a lo largo del tiempo. La acumulación de todas las nubes de puntos de una persona a lo largo del tiempo genera un conjunto de características de color de grandes dimensiones, denominadas características base, que proporcionan información útil de la apariencia de la persona. En esta Tesis se propone una familia de métodos para extraer información relevante de las características base. La primera propuesta compacta las características base en un vector único de color, denominado Bodyprint, que proporciona una buena generalización de la apariencia de la persona a lo largo del tiempo. En segundo lugar, se introducen los Bodyprints 3D, definidos como una extensión de los Bodyprints que incluyen información angular de las características de color. En tercer lugar, la apariencia de la persona se caracteriza mediante grupos de características de color que se generan independientemente a lo largo del tiempo. Este descriptor recibe el nombre de Grupos de Apariencias debido a su similitud con el concepto de Grupos de Palabras. Finalmente, se proponen diferentes modelos probabilísticos de variables latentes para reducir los vectores de características desde un punto de vista estadístico. La evaluación de los métodos demuestra que nuestras propuestas superan los métodos del estado del arte. / [CAT] La presència de sistemes de vigilància s'ha incrementat notòriament en els últims anys. Les xarxes de videovigilància poden veure's en quasi qualsevol espai públic i privat concorregut, la qual cosa genera una gran quantitat de dades de gran valor. L'anàlisi automàtic de la informació pren un paper important a l'hora d'extraure informació rellevant de l'escena. En particular, la re-identificaciò de persones és un camp que ha aconseguit gran interès durant els últims anys, especialment en seguretat i màrqueting. No obstant, hi ha certs factors, com variacions en les condicions d'il.luminació, variacions en la postura de la persona, oclusions o la presència d'artefactes que fan d'aquest camp un repte. Afortunadament, la introducció de noves tecnologies com les càmeres de profunditat, planteja nous paradigmes en la visió artificial i obri noves possibilitats. En aquesta Tesi es proposa un marc complet per abordar el problema de la re-identificació mitjançant càmeres rgb-profunditat. Aquest treball inclou l'anàlisi i avaluació de nous mètodes de segmentació, seguiment, descripció i emparellat de persones. Per tal d'avaluar les contribucions, s'ha creat una base de dades pública per re-identificació de persones emprant aquestes càmeres. Les càmeres rgb-profunditat proporcionen núvols de punts 3D amb informació de color. A partir de la informació de profunditat, es defineix i s'avalua un nou algorisme de segmentació de persones. Aquest mètode segmenta de forma precisa qualsevol persona en l'escena i resol de forma natural problemes d'oclusions i persones connectades. La màscara de segmentació d'una persona genera un núvol de punts 3D que pot ser fàcilment seguida al llarg del temps. L'acumulació de tots els núvols de punts d'una persona al llarg del temps genera un conjunt de característiques de color de grans dimensions, anomenades característiques base, que hi proporcionen informació útil de l'aparença de la persona. En aquesta Tesi es proposen una família de mètodes per extraure informació rellevant de les característiques base. La primera proposta compacta les característiques base en un vector únic de color, anomenat Bodyprint, que proporciona una bona generalització de l'aparença de la persona al llarg del temps. En segon lloc, s'introdueixen els Bodyprints 3D, definits com una extensió dels Bodyprints que inclouen informació angular de les característiques de color. En tercer lloc, l'aparença de la persona es caracteritza amb grups de característiques de color que es generen independentment a llarg del temps. Aquest descriptor reb el nom de Grups d'Aparences a causa de la seua similitud amb el concepte de Grups de Paraules. Finalment, es proposen diferents models probabilístics de variables latents per reduir els vectors de característiques des d'un punt de vista estadístic. L'avaluació dels mètodes demostra que les propostes presentades superen als mètodes de l'estat de l'art. / Oliver Moll, J. (2015). PERSON RE-IDENTIFICATION USING RGB-DEPTH CAMERAS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59227 / TESIS

This thesis focuses on the possibility of using current methods in the field of computer vision to re-identify graffiti tags. The work examines the possibility of using convolutional neural networks to re-identify graffiti tags, which are the most common type of graffiti. The work experimented with various models of convolutional neural networks, the most suitable of which was MobileNet using the triplet loss function, which managed to achieve a mAP of 36.02%.

With the increasing highlighted security concerns in Intelligent Transportation System (ITS), Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VReID). These components perform coarse-to-fine recognition tasks in three steps. The VAVR system can be widely used in suspicious vehicle recognition, urban traffic monitoring, and automated driving system. Vehicle recognition is complicated due to the subtle visual differences between different vehicle models. Therefore, how to build a VAVR system that can fast and accurately recognize vehicle information has gained tremendous attention. In this work, by taking advantage of the emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, we propose several models used for vehicle recognition. First, we propose a novel Recurrent Attention Unit (RAU) to expand the standard Convolutional Neural Network (CNN) architecture for VMMR. RAU learns to recognize the discriminative part of a vehicle on multiple scales and builds up a connection with the prominent information in a recurrent way. The proposed ResNet101-RAU achieves excellent recognition accuracy of 93.81% on the Stanford Cars dataset and 97.84% on the CompCars dataset. Second, to construct efficient vehicle recognition models, we simplify the structure of RAU and propose a Lightweight Recurrent Attention Unit (LRAU). The proposed LRAU extracts the discriminative part features by generating attention masks to locate the keypoints of a vehicle (e.g., logo, headlight). The attention mask is generated based on the feature maps received by the LRAU and the preceding attention state generated by the preceding LRAU. Then, by adding LRAUs to the standard CNN architectures, we construct three efficient VMMR models. Our models achieve the state-of-the-art results with 93.94% accuracy on the Stanford Cars dataset, 98.31% accuracy on the CompCars dataset, and 99.41% on the NTOU-MMR dataset. In addition, we construct a one-stage Vehicle Detection and Fine-grained Recognition (VDFG) model by combining our LRAU with the general object detection model. Results show the proposed VDFG model can achieve excellent performance with real-time processing speed. Third, to address the VReID task, we design the Compact Attention Unit (CAU). CAU has a compact structure, and it relies on a single attention map to extract the discriminative local features of a vehicle. We add two CAUs to the truncated ResNet to construct a small but efficient VReID model, ResNetT-CAU. Compared with the original ResNet, the model size of ResNetT-CAU is reduced by 60%. Extensive experiments on the VeRi and VehicleID dataset indicate the proposed ResNetT-CAU achieve the best re-identification results on both datasets. In summary, the experimental results on the challenging benchmark VMMR and VReID datasets indicate our models achieve the best VMMR and VReID performance, and our models have a small model size and fast image processing speed.

Person Re-Identification (ReID) is the process of associating images of the same person taken from different angles, cameras and at different times. The task is very challenging as a slight change in the appearance of the person can cause troubles in identifying them. In this thesis, the Re-Identification task is applied in the context of soccer games. In soccer games, the players of the same team wear the same outfit and colors, thus the task of Re-Identification is very hard. To address this problem, a state-of-the-art deep neural network based model named AlignedReID and a variation of it called Vanilla model are explored and compared to a baseline approach based on Euclidean distance in the image space. The AlignedReID model uses two feature extractor branches, one global and one local feature extractor. The Vanilla approach is a variation of the AlignedReID which uses only the global feature extractor branch of the AlignedReID. They are trained using two different loss functions, the Batch Hard and its soft-margin variation. The triplet loss is used, where for each loss calculation a triplet of images is used, an anchor, a positive pair (coming from the same person) and a negative pair. By comparing the metrics used for their evaluation, that is rank-1, rank-5, mean Average Precision (mAP) and the Area Under Curve (AUC), and by statistically comparing their mAPs which is assumed to be the most important metric, the AlignedReID model using the Batch Hard loss function outperforms the rest of the models with a mAP of 81\% and rank-1 \& rank-5 above 98\%. Also, a qualitative evaluation of the best model is presented using Grad-CAM, in order to figure how the model decides which images are similar by investigating in which parts of the images it focuses on to produce their embedding representations. It is observed that the model focuses on some discriminative features, such as face, legs and hands other than clothing color and outfit. The empirical results suggest that the AlignedReid is usable in real world applications, however further research to get a better understanding of the generalization to different cameras, leagues and other factors that may affect appearance would be interesting.

Multiple object tracking (MOT) is the task of detecting multiple objects in a scene and associating detections over time to form tracks. It is essential for many scene understanding tasks like surveillance, robotics and autonomous driving. Nowadays, the dominating tracking pipeline is to first detect all individual objects in a scene followed by a separate data association step, also known as tracking-by-detection. Recently, methods doing simultaneous detection and tracking has emerged, combining the task of detection and tracking into one single framework. In this project, we analyse performance of multiple object tracking algorithms belonging to both tracking categories. The goal is to examine strengths, weaknesses, and real-time capability of different tracking approaches in order to understand their suitability in different applications. Results show that a tracking-by-detection system with Scaled-YOLOv4 and SORT achieves 46.8% accuracy at over 28 frames per second (FPS) on Nvidia GTX 1080. By reducing the input resolution, inference speed is increased to almost 50 FPS, making it well suitable for real-time application. The addition of a deep re-identification CNN reduces the number of identity switches by 47%. However, association speed drops as low as 14 FPS for densely populated scenes. This indicates that re-identification CNNs may be impractical for safety critical applications like autonomous driving, especially in urban environments. Simultaneous detection and tracking results suggests an increased tracking robustness. The removal of a complex data association strategy improves robustness with respect to extended modules like re-identification. This indicates that the inherent simplicity in the simultaneous detection and tracking paradigm can provide robust baseline trackers for a variety of applications. We note that further research is required to strengthen this notion. / Multipel objektspårning handlar om att detektera alla objekt i bilder och associera dem över tid för att bilda spår. Det är ett viktigt ämne inom datorseende för flera applikationer, däribland kameraövervakning, robotik och självkörande fordon. Idag är det dominerande tillvägagångsättet inom objektspårning att först detektera alla objekt och sedan associera dem i ett separat steg, också kallat spårning-genom-detektion. På senare tid har det framkommit nya metoder som detekterar och spårar samtidigt. I detta projekt analyserar vi prestanda av metoder som tillämpar båda tillvägagångssätt. Målet med projektet är att undersöka styrkor, svagheter och hur väl metoderna lämpar sig för att användas i realtid. Detta för att förstå hur olika objektspårare kan anpassas till olika praktiska applikationer. Resultaten visar att ett system som tillämpar spårning-genom-detektion med Scaled-YOLOv4 och SORT, uppnår 46.8% noggrannhet med en hastighet på över 28 bildrutor per sekund. Detta på en Nvidia GTX 1080. Genom att minska bildupplösningen når hastigheten nästan hela vägen upp till 50 bildrutor per sekund, vilket gör systemet väl lämpat för realtidsapplikation. Genom att addera ett djupt nätverk för återidentifiering minskar antalet identitetsbyten med 47%. Samtidigt minskar också hastigheten för spårning till 14 bildrutor per sekund i välbefolkade miljöer. Detta indikerar att djupa nätverk för återidentifiering inte lämpar sig för säkerhetskritiska applikationer såsom självkörande fordon. Särskilt i urbana miljöer. Resultat för system som detekterar och spårar samtidigt antyder att de är mer robusta. Genom att ta bort komplexa strategier för associering blir systemen robusta mot ytterligare moduler såsom återidentifiering. Det ger en indikation på att den inneboende enkelheten i dessa system resulterar i objektspårare som kan fungera som grunder i många olika applikationer. Vi noterar att ytterligare forsking behövs för att styrka denna idé.

In this thesis I propose methods and strategies for the design of advanced model predictive control designs. The contributions are in the areas of data-driven model based MPC, model monitoring and explicit incorporation of closed-loop response considerations in the MPC, while handling issues such as plant-model mismatch, constraints and uncertainty. In the initial phase of this research, I address the problem of handling plant-model mismatch by designing a subspace identification based MPC framework that includes model monitoring and closed-loop identification components. In contrast to performance monitoring based approaches, the validity of the underlying model is monitored by proposing two indexes that compare model predictions with measured past output. In the event that the model monitoring threshold is breached, a new model is identified using an adapted closed-loop subspace identification method. To retain the knowledge of the nominal system dynamics, the proposed approach uses the past training data and current input, output and set-point as the training data for re-identification. A model validity mechanism then checks if the new model predictions are better than the existing model, and if they are, then the new model is utilized within the MPC. Next, the proposed MPC with re-identification method is extended to batch processes. To this end, I first utilize a subspace-based model identification approach for batch processes to be used in model predictive control. A model performance index is developed for batch process, then in the case of poor prediction, re-identification is triggered to identify a new model. In order to emphasize on the recent batch data, the identification is developed in order to increase the contribution of the current data. In another direction, the stability of data driven predictive control is addressed. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. Finally, I address the problem of control of nonlinear systems to deliver a prescribed closed-loop behavior. In particular, the framework allows for the practitioner to first specify the nature and specifics of the desired closed-loop behavior (e.g., first order with smallest time constant, second order with no more than a certain percentage overshoot, etc.). An optimization based formulation then computes the control action to deliver the best attainable closed loop behavior. To decouple the problems of determining the best attainable behavior and tracking it as closely as possible, the optimization problem is posed and solved in two tiers. In the first tier, the focus is on determining the best closed-loop behavior attainable, subject to stability and tracking constraints. In the second tier, the inputs are tweaked to possibly improve the tracking of the optimal output trajectories given by the first tier. The effectiveness of all of the proposed methods are illustrated through simulations on nonlinear systems. / Dissertation / Doctor of Philosophy (PhD)