Real-time people tracking in a camera network

Visual tracking is a fundamental key to the recognition and analysis of human behaviour. In this thesis we present an approach to track several subjects using multiple cameras in real time. The tracking framework employs a numerical Bayesian estimator, also known as a particle lter, which has been developed for parallel implementation on a Graphics Processing Unit (GPU). In order to integrate multiple cameras into a single tracking unit we represent the human body by a parametric ellipsoid in a 3D world. The elliptical boundary can be projected rapidly, several hundred times per subject per frame, onto any image for comparison with the image data within a likelihood model. Adding variables to encode visibility and persistence into the state vector, we tackle the problems of distraction and short-period occlusion. However, subjects may also disappear for longer periods due to blind spots between cameras elds of view. To recognise a desired subject after such a long-period, we add coloured texture to the ellipsoid surface, which is learnt and retained during the tracking process. This texture signature improves the recall rate from 60% to 70-80% when compared to state only data association. Compared to a standard Central Processing Unit (CPU) implementation, there is a signi cant speed-up ratio

Motion-Augmented Inference and Joint Kernels in Structured Learning for Object Tracking and Integration with Object Segmentation

Video object tracking is a fundamental task of continuously following an object of interest in a video sequence. It has attracted considerable attention in both academia and industry due to its diverse applications, such as in automated video surveillance, augmented and virtual reality, medical, automated vehicle navigation and tracking, and smart devices. Challenges in video object tracking arise from occlusion, deformation, background clutter, illumination variation, fast object motion, scale variation, low resolution, rotation, out-of-view, and motion blur. Object tracking remains, therefore, as an active research field. This thesis explores improving object tracking by employing 1) advanced techniques in machine learning theory to account for intrinsic changes in the object appearance under those challenging conditions, and 2) object segmentation. More specifically, we propose a fast and competitive method for object tracking by modeling target dynamics as a random stochastic process, and using structured support vector machines. First, we predict target dynamics by harmonic means and particle filter in which we exploit kernel machines to derive a new entropy based observation likelihood distribution. Second, we employ online structured support vector machines to model object appearance, where we analyze responses of several kernel functions for various feature descriptors and study how such kernels can be optimally combined to formulate a single joint kernel function. During learning, we develop a probability formulation to determine model updates and use sequential minimal optimization-step to solve the structured optimization problem. We gain efficiency improvements in the proposed object tracking by 1) exploiting particle filter for sampling the search space instead of commonly adopted dense sampling strategies, and 2) introducing a motion-augmented regularization term during inference to constrain the output search space. We then extend our baseline tracker to detect tracking failures or inaccuracies and reinitialize itself when needed. To that end, we integrate object segmentation into tracking. First, we use binary support vector machines to develop a technique to detect tracking failures (or inaccuracies) by monitoring internal variables of our baseline tracker. We leverage learned examples from our baseline tracker to train the employed binary support vector machines. Second, we propose an automated method to re-initialize the tracker to recover from tracking failures by integrating an active contour based object segmentation and using particle filter to sample bounding boxes for segmentation. Through extensive experiments on standard video datasets, we subjectively and objectively demonstrate that both our baseline and extended methods strongly compete against state-of-the-art object tracking methods on challenging video conditions

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Novel Texture-based Probabilistic Object Recognition and Tracking Techniques for Food Intake Analysis and Traffic Monitoring

More complex image understanding algorithms are increasingly practical in a host of emerging applications. Object tracking has value in surveillance and data farming; and object recognition has applications in surveillance, data management, and industrial automation. In this work we introduce an object recognition application in automated nutritional intake analysis and a tracking application intended for surveillance in low quality videos. Automated food recognition is useful for personal health applications as well as nutritional studies used to improve public health or inform lawmakers. We introduce a complete, end-to-end system for automated food intake measurement. Images taken by a digital camera are analyzed, plates and food are located, food type is determined by neural network, distance and angle of food is determined and 3D volume estimated, the results are cross referenced with a nutritional database, and before and after meal photos are compared to determine nutritional intake. We compare against contemporary systems and provide detailed experimental results of our system\u27s performance. Our tracking systems consider the problem of car and human tracking on potentially very low quality surveillance videos, from fixed camera or high flying \acrfull{uav}. Our agile framework switches among different simple trackers to find the most applicable tracker based on the object and video properties. Our MAPTrack is an evolution of the agile tracker that uses soft switching to optimize between multiple pertinent trackers, and tracks objects based on motion, appearance, and positional data. In both cases we provide comparisons against trackers intended for similar applications i.e., trackers that stress robustness in bad conditions, with competitive results

A probabilistic integrated object recognition and tracking framework for video sequences

Recognition and tracking of multiple objects in video sequences is one of the main challenges in computer vision that currently deserves a lot of attention from researchers. Almost all the reported approaches are very application-dependent and there is a lack of a general methodology for dynamic object recognition and tracking that can be instantiated in particular cases. In this thesis, the work is oriented towards the definition and development of such a methodology which integrates object recognition and tracking from a general perspective using a probabilistic framework called PIORT (probabilistic integrated object recognition and tracking framework). It include some modules for which a variety of techniques and methods can be applied. Some of them are well-known but other methods have been designed, implemented and tested during the development of this thesis.The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB colour features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods. All these methods have been tested experimentally in several test video sequences taken with still and moving cameras and including full and partial occlusions of the tracked object in indoor and outdoor scenarios in a variety of cases with different levels of task complexity. This allowed the evaluation of the general methodology and the alternative methods that compose these modules.A Probabilistic Integrated Object Recognition and Tracking Framework for Video SequencesEl reconocimiento y seguimiento de múltiples objetos en secuencias de vídeo es uno de los principales desafíos en visión por ordenador que actualmente merece mucha atención de los investigadores. Casi todos los enfoques reportados son muy dependientes de la aplicación y hay carencia de una metodología general para el reconocimiento y seguimiento dinámico de objetos, que pueda ser instanciada en casos particulares. En esta tesis, el trabajo esta orientado hacia la definición y desarrollo de tal metodología, la cual integra reconocimiento y seguimiento de objetos desde una perspectiva general usando un marco probabilístico de trabajo llamado PIORT (Probabilistic Integrated Object Recognition and Tracking). Este incluye algunos módulos para los que se puede aplicar una variedad de técnicas y métodos. Algunos de ellos son bien conocidos, pero otros métodos han sido diseñados, implementados y probados durante el desarrollo de esta tesis.El primer paso en el marco de trabajo propuesto es un módulo estático de reconocimiento que provee probabilidades de clase para cada píxel de la imagen desde un conjunto de características locales. Estas probabilidades son actualizadas dinámicamente y suministradas a un modulo decisión de seguimiento capaz de manejar oclusiones parciales o totales. Se presenta dos métodos específicos usando características de color RGB pero diferentes en la implementación del clasificador: uno es un método Bayesiano basado en la máxima verosimilitud y el otro método está basado en una red neuronal. Los resultados experimentales obtenidos han mostrado que, por una parte, el enfoque basado en la red neuronal funciona similarmente y algunas veces mejor que el enfoque bayesiano cuando son integrados dentro del marco probabilístico de seguimiento. Por otra parte, nuestro método PIORT ha alcanzado mejores resultados comparando con otros métodos de seguimiento publicados. Todos estos métodos han sido probados experimentalmente en varias secuencias de vídeo tomadas con cámaras fijas y móviles incluyendo oclusiones parciales y totales del objeto a seguir, en ambientes interiores y exteriores, en diferentes tareas y niveles de complejidad. Esto ha permitido evaluar tanto la metodología general como los métodos alternativos que componen sus módulos

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Human Pose Estimation with Supervoxels

This thesis investigates how segmentation as a preprocessing step can reduce both the search space as well as complexity of human pose estimation in the context of smart environments. A 3D reconstruction is computed with a voxel carving algorithm. Based on a superpixel algorithm, these voxels are segmented into supervoxels that are then applied to pictorial structures in 3D to efficiently estimate the human pose. Both static and dynamic gesture recognition applications were developed

Augmentieren von Personen in Monokularen Videodaten

When aiming at realistic video augmentation, i.e. the embedding of virtual, 3-dimensional objects into a scene's original content, a series of challenging problems has to be solved. This is especially the case when working with solely monocular input material, as important additional 3D information is missing and has to be recovered during the process, if necessary. In this work, I will present a semi-automatic strategy to tackle this task by providing solutions to individual problems in the context of virtual clothing as an example for realistic video augmentation. Starting with two different approaches for monocular pose and motion estimation, I will show how to build a 3D human body model by estimating detailed shape information as well as basic surface material properties. This information allows to further extract a dynamic illumination model from the provided input material. The illumination model is particularly important for rendering a realistic virtual object and adds a lot of realism to the final video augmentation. The animated human model is able to interact with virtual 3D objects and is used in the context of virtual clothing to animate simulated garments. To achieve the desired realism, I present an additional image-based compositing approach that realistically embeds the simulated garment into the original scene content. Combining the presented approaches provide an integrated strategy for realistic augmentation of actors in monocular video sequences.Unter der Zielsetzung einer realistischen Videoaugmentierung durch das Einbetten virtueller, dreidimensionaler Objekte in eine bestehende Videoaufnahme, gibt eine Reihe interessanter und schwieriger Problemen zu lösen. Besonders im Hinblick auf die Verarbeitung monokularer Eingabedaten fehlen wichtige räumliche Informationen, welche aus den zweidimensionalen Eingabedaten rekonstruiert werden müssen. In dieser Arbeit präsentiere ich eine halbautomatische Verfahrensweise, welche es ermöglicht, die einzelnen Teilprobleme einer umfassenden Videoaugmentierung nacheinander in einer integrierten Strategie zu lösen. Dies demonstriere ich am Beispiel von virtueller Kleidung. Beginnend mit zwei unterschiedlichen Ansätzen zur Posen- und Bewegungsrekonstruktion wird ein realistisches 3D Körpermodell eines Menschen erzeugt. Dazu wird die detaillierte Körperform durch ein geeignetes Verfahren approximiert und eine Rekonstruktion der Oberflächenmaterialen vorgenommen. Diese Informationen werden unter anderem dazu verwendet, aus dem Eingabevideo eine dynamische Szenenbeleuchtung zu rekonstruieren. Die Beleuchtungsinformationen sind besonders wichtig für eine realistische Videoaugmentierung, da gerade eine korrekte Beleuchtung den Realitätsgrad des virtuell generierten Objektes erhöht. Das rekonstruierte und animierte Körpermodell ist durch seinen Detailgrad in der Lage, mit virtuellen Objekten zu interagieren. Dies kommt besonders im Anwendungsfall von virtueller Kleidung zum tragen. Um den gewünschten Realitätsgrad zu erreichen, führe ich ein zusätzliches, bild-basiertes Korrekturverfahren ein, welches hilft, die finale Bildkomposition zu optimieren. Die Kombination aller präsentierter Teilverfahren bildet eine vollumfängliche Strategie zur Augmentierung von monokularem Videomaterial, die zur realistischen Simulation und Einbettung von virtueller Kleidung eines Schauspielers im Originalvideo verwendet werden kann

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture