The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker

Reliable collision avoidance is one of the main requirements for autonomous driving. Hence, it is important to correctly estimate the states of an unknown number of static and dynamic objects in real-time. Here, data association is a major challenge for every multi-target tracker. We propose a novel multi-target tracker called Greedy Dirichlet Process Filter (GDPF) based on the non-parametric Bayesian model called Dirichlet Processes and the fast posterior computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a temporal dependence we get a real-time capable tracking framework without the need of a previous clustering or data association step. Real-world tests show that GDPF outperforms other multi-target tracker in terms of accuracy and stability

Bayesian non-parametrics for multi-modal segmentation

Segmentation is a fundamental and core problem in computer vision research which has applications in many tasks, such as object recognition, content-based image retrieval, and semantic labelling. To partition the data into groups coherent in one or more characteristics such as semantic classes, is often a first step towards understanding the content of data. As information in the real world is generally perceived in multiple modalities, segmentation performed on multi-modal data for extracting the latent structure usually encounters a challenge: how to combine features from multiple modalities and resolve accidental ambiguities. This thesis tackles three main axes of multi-modal segmentation problems: video segmentation and object discovery, activity segmentation and discovery, and segmentation in 3D data. For the first two axes, we introduce non-parametric Bayesian approaches for segmenting multi-modal data collections, including groups of videos and context sensor streams. The proposed method shows benefits on: integrating multiple features and data dependencies in a probabilistic formulation, inferring the number of clusters from data and hierarchical semantic partitions, as well as resolving ambiguities by joint segmentation across videos or streams. The third axis focuses on the robust use of 3D information for various applications, as 3D perception provides richer geometric structure and holistic observation of the visual scene. The studies covered in this thesis for utilizing various types of 3D data include: 3D object segmentation based on Kinect depth sensing improved by cross-modal stereo, matching 3D CAD models to objects on 2D image plane by exploiting the differentiability of the HOG descriptor, segmenting stereo videos based on adaptive ensemble models, and fusing 2D object detectors with 3D context information for an augmented reality application scenario.Segmentierung ist ein zentrales problem in der Computer Vision Forschung mit Anwendungen in vielen Bereichen wie der Objekterkennung, der inhaltsbasierten Bildsuche und dem semantischen Labelling. Daten in Gruppen zu partitionieren, die in einer oder mehreren Eigenschaften wie zum Beispiel der semantischen Klasse übereinstimmen, ist oft ein erster Schritt in Richtung Inhaltsanalyse. Da Informationen in der realen Welt im Allgemeinen multi-modal wahrgenommen werden, wird die Segmentierung auf multi-modale Daten angewendet und die latente Struktur dahinter extrahiert. Dies stellt in der Regel eine Herausforderung dar: Wie kombiniert man Merkmale aus mehreren Modalitäten und beseitigt zufällige Mehrdeutigkeiten? Diese Doktorarbeit befasst sich mit drei Hauptachsen multi-modaler Segmentierungsprobleme: Videosegmentierung und Objektentdeckung, Aktivitätssegmentierung und –entdeckung, sowie Segmentierung von 3D Daten. Für die ersten beiden Achsen führen wir nichtparametrische Bayessche Ansätze ein um multi-modale Datensätze wie Videos und Kontextsensor-Ströme zu segmentieren. Die vorgeschlagene Methode zeigt Vorteile in folgenden Bereichen: Integration multipler Merkmale und Datenabhängigkeiten in probabilistischen Formulierungen, Bestimmung der Anzahl der Cluster und hierarchische, semantischen Partitionen, sowie die Beseitigung von Mehrdeutigkeiten in gemeinsamen Segmentierungen in Videos und Sensor-Strömen. Die dritte Achse konzentiert sich auf die robuste Nutzung von 3D Informationen für verschiedene Anwendungen. So bietet die 3D-Wahrnehmung zum Beispiel reichere geometrische Strukturen und eine holistische Betrachtung der sichtbaren Szene. Die Untersuchungen, die in dieser Arbeit zur Nutzung verschiedener Arten von 3D-Daten vorgestellt werden, umfassen: die 3D-Objektsegmentierung auf Basis der Kinect Tiefenmessung, verbessert durch cross-modale Stereoverfahren, die Anpassung von 3D-CAD-Modellen auf Objekte in der 2D-Bildebene durch Ausnutzung der Differenzierbarkeit des HOG-Descriptors, die Segmentierung von Stereo-Videos, basierend auf adaptiven Ensemble-Modellen, sowie der Verschmelzung von 2D- Objektdetektoren mit 3D-Kontextinformationen für ein Augmented-Reality Anwendungsszenario

Bayesian non-parametrics for multi-modal segmentation

Segmentation is a fundamental and core problem in computer vision research which has applications in many tasks, such as object recognition, content-based image retrieval, and semantic labelling. To partition the data into groups coherent in one or more characteristics such as semantic classes, is often a first step towards understanding the content of data. As information in the real world is generally perceived in multiple modalities, segmentation performed on multi-modal data for extracting the latent structure usually encounters a challenge: how to combine features from multiple modalities and resolve accidental ambiguities. This thesis tackles three main axes of multi-modal segmentation problems: video segmentation and object discovery, activity segmentation and discovery, and segmentation in 3D data. For the first two axes, we introduce non-parametric Bayesian approaches for segmenting multi-modal data collections, including groups of videos and context sensor streams. The proposed method shows benefits on: integrating multiple features and data dependencies in a probabilistic formulation, inferring the number of clusters from data and hierarchical semantic partitions, as well as resolving ambiguities by joint segmentation across videos or streams. The third axis focuses on the robust use of 3D information for various applications, as 3D perception provides richer geometric structure and holistic observation of the visual scene. The studies covered in this thesis for utilizing various types of 3D data include: 3D object segmentation based on Kinect depth sensing improved by cross-modal stereo, matching 3D CAD models to objects on 2D image plane by exploiting the differentiability of the HOG descriptor, segmenting stereo videos based on adaptive ensemble models, and fusing 2D object detectors with 3D context information for an augmented reality application scenario.Segmentierung ist ein zentrales problem in der Computer Vision Forschung mit Anwendungen in vielen Bereichen wie der Objekterkennung, der inhaltsbasierten Bildsuche und dem semantischen Labelling. Daten in Gruppen zu partitionieren, die in einer oder mehreren Eigenschaften wie zum Beispiel der semantischen Klasse übereinstimmen, ist oft ein erster Schritt in Richtung Inhaltsanalyse. Da Informationen in der realen Welt im Allgemeinen multi-modal wahrgenommen werden, wird die Segmentierung auf multi-modale Daten angewendet und die latente Struktur dahinter extrahiert. Dies stellt in der Regel eine Herausforderung dar: Wie kombiniert man Merkmale aus mehreren Modalitäten und beseitigt zufällige Mehrdeutigkeiten? Diese Doktorarbeit befasst sich mit drei Hauptachsen multi-modaler Segmentierungsprobleme: Videosegmentierung und Objektentdeckung, Aktivitätssegmentierung und –entdeckung, sowie Segmentierung von 3D Daten. Für die ersten beiden Achsen führen wir nichtparametrische Bayessche Ansätze ein um multi-modale Datensätze wie Videos und Kontextsensor-Ströme zu segmentieren. Die vorgeschlagene Methode zeigt Vorteile in folgenden Bereichen: Integration multipler Merkmale und Datenabhängigkeiten in probabilistischen Formulierungen, Bestimmung der Anzahl der Cluster und hierarchische, semantischen Partitionen, sowie die Beseitigung von Mehrdeutigkeiten in gemeinsamen Segmentierungen in Videos und Sensor-Strömen. Die dritte Achse konzentiert sich auf die robuste Nutzung von 3D Informationen für verschiedene Anwendungen. So bietet die 3D-Wahrnehmung zum Beispiel reichere geometrische Strukturen und eine holistische Betrachtung der sichtbaren Szene. Die Untersuchungen, die in dieser Arbeit zur Nutzung verschiedener Arten von 3D-Daten vorgestellt werden, umfassen: die 3D-Objektsegmentierung auf Basis der Kinect Tiefenmessung, verbessert durch cross-modale Stereoverfahren, die Anpassung von 3D-CAD-Modellen auf Objekte in der 2D-Bildebene durch Ausnutzung der Differenzierbarkeit des HOG-Descriptors, die Segmentierung von Stereo-Videos, basierend auf adaptiven Ensemble-Modellen, sowie der Verschmelzung von 2D- Objektdetektoren mit 3D-Kontextinformationen für ein Augmented-Reality Anwendungsszenario

Long-term future prediction under uncertainty and multi-modality

Humans have an innate ability to excel at activities that involve prediction of complex object dynamics such as predicting the possible trajectory of a billiard ball after it has been hit by the player or the prediction of motion of pedestrians while on the road. A key feature that enables humans to perform such tasks is anticipation. There has been continuous research in the area of Computer Vision and Artificial Intelligence to mimic this human ability for autonomous agents to succeed in the real world scenarios. Recent advances in the field of deep learning and the availability of large scale datasets has enabled the pursuit of fully autonomous agents with complex decision making abilities such as self-driving vehicles or robots. One of the main challenges encompassing the deployment of these agents in the real world is their ability to perform anticipation tasks with at least human level efficiency. To advance the field of autonomous systems, particularly, self-driving agents, in this thesis, we focus on the task of future prediction in diverse real world settings, ranging from deterministic scenarios such as prediction of paths of balls on a billiard table to the predicting the future of non-deterministic street scenes. Specifically, we identify certain core challenges for long-term future prediction: long-term prediction, uncertainty, multi-modality, and exact inference. To address these challenges, this thesis makes the following core contributions. Firstly, for accurate long-term predictions, we develop approaches that effectively utilize available observed information in the form of image boundaries in videos or interactions in street scenes. Secondly, as uncertainty increases into the future in case of non-deterministic scenarios, we leverage Bayesian inference frameworks to capture calibrated distributions of likely future events. Finally, to further improve performance in highly-multimodal non-deterministic scenarios such as street scenes, we develop deep generative models based on conditional variational autoencoders as well as normalizing flow based exact inference methods. Furthermore, we introduce a novel dataset with dense pedestrian-vehicle interactions to further aid the development of anticipation methods for autonomous driving applications in urban environments.Menschen haben die angeborene Fähigkeit, Vorgänge mit komplexer Objektdynamik vorauszusehen, wie z. B. die Vorhersage der möglichen Flugbahn einer Billardkugel, nachdem sie vom Spieler gestoßen wurde, oder die Vorhersage der Bewegung von Fußgängern auf der Straße. Eine Schlüsseleigenschaft, die es dem Menschen ermöglicht, solche Aufgaben zu erfüllen, ist die Antizipation. Im Bereich der Computer Vision und der Künstlichen Intelligenz wurde kontinuierlich daran geforscht, diese menschliche Fähigkeit nachzuahmen, damit autonome Agenten in der realen Welt erfolgreich sein können. Jüngste Fortschritte auf dem Gebiet des Deep Learning und die Verfügbarkeit großer Datensätze haben die Entwicklung vollständig autonomer Agenten mit komplexen Entscheidungsfähigkeiten wie selbstfahrende Fahrzeugen oder Roboter ermöglicht. Eine der größten Herausforderungen beim Einsatz dieser Agenten in der realen Welt ist ihre Fähigkeit, Antizipationsaufgaben mit einer Effizienz durchzuführen, die mindestens der menschlichen entspricht. Um das Feld der autonomen Systeme, insbesondere der selbstfahrenden Agenten, voranzubringen, konzentrieren wir uns in dieser Arbeit auf die Aufgabe der Zukunftsvorhersage in verschiedenen realen Umgebungen, die von deterministischen Szenarien wie der Vorhersage der Bahnen von Kugeln auf einem Billardtisch bis zur Vorhersage der Zukunft von nicht-deterministischen Straßenszenen reichen. Insbesondere identifizieren wir bestimmte grundlegende Herausforderungen für langfristige Zukunftsvorhersagen: Langzeitvorhersage, Unsicherheit, Multimodalität und exakte Inferenz. Um diese Herausforderungen anzugehen, leistet diese Arbeit die folgenden grundlegenden Beiträge. Erstens: Für genaue Langzeitvorhersagen entwickeln wir Ansätze, die verfügbare Beobachtungsinformationen in Form von Bildgrenzen in Videos oder Interaktionen in Straßenszenen effektiv nutzen. Zweitens: Da die Unsicherheit in der Zukunft bei nicht-deterministischen Szenarien zunimmt, nutzen wir Bayes’sche Inferenzverfahren, um kalibrierte Verteilungen wahrscheinlicher zukünftiger Ereignisse zu erfassen. Drittens: Um die Leistung in hochmultimodalen, nichtdeterministischen Szenarien wie Straßenszenen weiter zu verbessern, entwickeln wir tiefe generative Modelle, die sowohl auf konditionalen Variations-Autoencodern als auch auf normalisierenden fließenden exakten Inferenzmethoden basieren. Darüber hinaus stellen wir einen neuartigen Datensatz mit dichten Fußgänger-Fahrzeug- Interaktionen vor, um Antizipationsmethoden für autonome Fahranwendungen in urbanen Umgebungen weiter zu entwickeln

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Visual Perception System for Aerial Manipulation: Methods and Implementations

La tecnología se evoluciona a gran velocidad y los sistemas autónomos están empezado a ser una realidad. Las compañías están demandando, cada vez más, soluciones robotizadas para mejorar la eficiencia de sus operaciones. Este también es el caso de los robots aéreos. Su capacidad única de moverse libremente por el aire los hace excelentes para muchas tareas que son tediosas o incluso peligrosas para operadores humanos. Hoy en día, la gran cantidad de sensores y drones comerciales los hace soluciones muy tentadoras. Sin embargo, todavía se requieren grandes esfuerzos de obra humana para customizarlos para cada tarea debido a la gran cantidad de posibles entornos, robots y misiones. Los investigadores diseñan diferentes algoritmos de visión, hardware y sensores para afrontar las diferentes tareas. Actualmente, el campo de la robótica manipuladora aérea está emergiendo con el objetivo de extender la cantidad de aplicaciones que estos pueden realizar. Estas pueden ser entre otras, inspección, mantenimiento o incluso operar válvulas u otras máquinas. Esta tesis presenta un sistema de manipulación aérea y un conjunto de algoritmos de percepción para la automatización de las tareas de manipulación aérea. El diseño completo del sistema es presentado y una serie de frameworks son presentados para facilitar el desarrollo de este tipo de operaciones. En primer lugar, la investigación relacionada con el análisis de objetos para manipulación y planificación de agarre considerando diferentes modelos de objetos es presentado. Dependiendo de estos modelos de objeto, se muestran diferentes algoritmos actuales de análisis de agarre y algoritmos de planificación para manipuladores simples y manipuladores duales. En Segundo lugar, el desarrollo de algoritmos de percepción para detección de objetos y estimación de su posicione es presentado. Estos permiten al sistema identificar objetos de cualquier tipo en cualquier escena para localizarlos para efectuar las tareas de manipulación. Estos algoritmos calculan la información necesaria para los análisis de manipulación descritos anteriormente. En tercer lugar. Se presentan algoritmos de visión para localizar el robot en el entorno al mismo tiempo que se elabora un mapa local, el cual es beneficioso para las tareas de manipulación. Estos mapas se enriquecen con información semántica obtenida en los algoritmos de detección. Por último, se presenta el desarrollo del hardware relacionado con la plataforma aérea, el cual incluye unos manipuladores de bajo peso y la invención de una herramienta para realizar tareas de contacto con superficies rígidas que sirve de estimador de la posición del robot. Todas las técnicas presentadas en esta tesis han sido validadas con extensiva experimentación en plataformas reales.Technology is growing fast, and autonomous systems are becoming a reality. Companies are increasingly demanding robotized solutions to improve the efficiency of their operations. It is also the case for aerial robots. Their unique capability of moving freely in the space makes them suitable for many tasks that are tedious and even dangerous for human operators. Nowadays, the vast amount of sensors and commercial drones makes them highly appealing. However, it is still required a strong manual effort to customize the existing solutions to each particular task due to the number of possible environments, robot designs and missions. Different vision algorithms, hardware devices and sensor setups are usually designed by researchers to tackle specific tasks. Currently, aerial manipulation is being intensively studied to allow aerial robots to extend the number of applications. These could be inspection, maintenance, or even operating valves or other machines. This thesis presents an aerial manipulation system and a set of perception algorithms for the automation aerial manipulation tasks. The complete design of the system is presented and modular frameworks are shown to facilitate the development of these kind of operations. At first, the research about object analysis for manipulation and grasp planning considering different object models is presented. Depend on the model of the objects, different state of art grasping analysis are reviewed and planning algorithms for both single and dual manipulators are shown. Secondly, the development of perception algorithms for object detection and pose estimation are presented. They allows the system to identify many kind of objects in any scene and locate them to perform manipulation tasks. These algorithms produce the necessary information for the manipulation analysis described in the previous paragraph. Thirdly, it is presented how to use vision to localize the robot in the environment. At the same time, local maps are created which can be beneficial for the manipulation tasks. These maps are are enhanced with semantic information from the perception algorithm mentioned above. At last, the thesis presents the development of the hardware of the aerial platform which includes the lightweight manipulators and the invention of a novel tool that allows the aerial robot to operate in contact with static objects. All the techniques presented in this thesis have been validated throughout extensive experimentation with real aerial robotic platforms