Gaze and Peripheral Vision Analysis for Human-Environment Interaction: Applications in Automotive and Mixed-Reality Scenarios

This thesis studies eye-based user interfaces which integrate information about the user’s perceptual focus-of-attention into multimodal systems to enrich the interaction with the surrounding environment. We examine two new modalities: gaze input and output in the peripheral field of view. All modalities are considered in the whole spectrum of the mixed-reality continuum. We show the added value of these new forms of multimodal interaction in two important application domains: Automotive User Interfaces and Human-Robot Collaboration. We present experiments that analyze gaze under various conditions and help to design a 3D model for peripheral vision. Furthermore, this work presents several new algorithms for eye-based interaction, like deictic reference in mobile scenarios, for non-intrusive user identification, or exploiting the peripheral field view for advanced multimodal presentations. These algorithms have been integrated into a number of software tools for eye-based interaction, which are used to implement 15 use cases for intelligent environment applications. These use cases cover a wide spectrum of applications, from spatial interactions with a rapidly changing environment from within a moving vehicle, to mixed-reality interaction between teams of human and robots.In dieser Arbeit werden blickbasierte Benutzerschnittstellen untersucht, die Infor- mationen ¨uber das Blickfeld des Benutzers in multimodale Systeme integrieren, um neuartige Interaktionen mit der Umgebung zu erm¨oglichen. Wir untersuchen zwei neue Modalit¨aten: Blickeingabe und Ausgaben im peripheren Sichtfeld. Alle Modalit¨aten werden im gesamten Spektrum des Mixed-Reality-Kontinuums betra- chtet. Wir zeigen die Anwendung dieser neuen Formen der multimodalen Interak- tion in zwei wichtigen Dom¨anen auf: Fahrerassistenzsysteme und Werkerassistenz bei Mensch-Roboter-Kollaboration. Wir pr¨asentieren Experimente, die blickbasierte Benutzereingaben unter verschiedenen Bedingungen analysieren und helfen, ein 3D- Modell f¨ur das periphere Sehen zu entwerfen. Dar¨uber hinaus stellt diese Arbeit mehrere neue Algorithmen f¨ur die blickbasierte Interaktion vor, wie die deiktis- che Referenz in mobilen Szenarien, die nicht-intrusive Benutzeridentifikation, oder die Nutzung des peripheren Sichtfeldes f¨ur neuartige multimodale Pr¨asentationen. Diese Algorithmen sind in eine Reihe von Software-Werkzeuge integriert, mit de- nen 15 Anwendungsf¨alle f¨ur intelligente Umgebungen implementiert wurden. Diese Demonstratoren decken ein breites Anwendungsspektrum ab: von der r¨aumlichen In- teraktionen aus einem fahrenden Auto heraus bis hin zu Mixed-Reality-Interaktionen zwischen Mensch-Roboter-Teams

Virtual reality based multi-modal teleoperation using mixed autonomy

The thesis presents a multi modal teleoperation interface featuring an integrated virtual reality based simulation aumented by sensors and image processing capabilities onboard the remotely operated vehicle. The virtual reality interface fuses an existing VR model with live video feed and prediction states, thereby creating a multi modal control interface. Virtual reality addresses the typical limitations of video-based teleoperation caused by signal lag and limited field of view thereby allowing the operator to navigate in a continuous fashion. The vehicle incorporates an on-board computer and a stereo vision system to facilitate obstacle detection. A vehicle adaptation system with a priori risk maps and real state tracking system enables temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle\u27s teleoperated state. As both the vehicle and the operator share absolute autonomy in stages, the operation is referred to as mixed autonomous. Finally, the system provides real time update of the virtual environment based on anomalies encountered by the vehicle. The system effectively balances the autonomy between the human operator and on board vehicle intelligence. The reliability results of individual components along with overall system implementation and the results of the user study helps show that the VR based multi modal teleoperation interface is more adaptable and intuitive when compared to other interfaces

Multi-target tracking using appearance models for identity maintenance

This thesis considers perception systems for urban environments. It focuses on the task of tracking dynamic objects and in particular on methods that can maintain the identities of targets through periods of ambiguity. Examples of such ambiguous situations occur when targets interact with each other, or when they are occluded by other objects or the environment. With the development of self driving cars, the push for autonomous delivery of packages, and an increasing use of technology for security, surveillance and public-safety applications, robust perception in crowded urban spaces is more important than ever before. A critical part of perception systems is the ability to understand the motion of objects in a scene. Tracking strategies that merge closely-spaced targets together into groups have been shown to offer improved robustness, but in doing so sacrifice the concept of target identity. Additionally, the primary sensor used for the tracking task may not provide the information required to reason about the identity of individual objects. There are three primary contributions in this work. The first is the development of 3D lidar tracking methods with improved ability to track closely-spaced targets and that can determine when target identities have become ambiguous. Secondly, this thesis defines appearance models suitable for the task of determining the identities of previously-observed targets, which may include the use of data from additional sensing modalities. The final contribution of this work is the combination of lidar tracking and appearance modelling, to enable the clarification of target identities in the presence of ambiguities caused by scene complexity. The algorithms presented in this work are validated on both carefully controlled and unconstrained datasets. The experiments show that in complex dynamic scenes with interacting targets, the proposed methods achieve significant improvements in tracking performance

Experimental Studies on the Reactive Thrust of the Mobile Robot of Arbitrary Orientation

The problem of creating mobile robots of arbitrary orientation in the technological space is to ensure reliable retention of robots on the surface of any orientation. Therefore, well-known experimental studies are mainly devoted to the creation of systems for coupling the robot to the surface along which it moves. The purpose of this study is to create a device for compensating the gravitational load of a mobile robot. The article contains the results of experimental testing of a fundamentally new approach to counteract the gravitational load of a mobile robot, namely, the expediency of equipping the robot with a source of reactive thrust of a non-chemical origin. A pneumatic generator of aerodynamic lift is proposed as such a source. Such a force partially compensates or completely overcomes the gravitational load, while not allowing the transformation of a mobile robot into an aircraft. The specified condition is necessary to perform contact power technological operations in the maintenance of various industrial facilities. In other words, the thrust force should not exceed the adhesion forces of the mobile robot to the displacement surface. As a research method, a full factorial experiment of the operation of a jet thrust generator was used, which is a new way to increase the reliability of holding the robot on an arbitrary surface. The article describes the methodology and description of the full factorial experiment with varying independent factors at two extreme levels. As a result, an experimental solution to the problem of finding the quasi-optimal values of the aerodynamic lifting force depending on the parameters of the jet thrust generator is obtained. As a result, the combination of a new robot design with the results of experimental studies confirms the feasibility of using a pneumatic jet thrust generator as a means of increasing the reliability of holding mobile robots on an arbitrary orientation surface in the technological space

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

A Survey on Physical Adversarial Attack in Computer Vision

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substantial enhancements in traditional tasks. However, deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples crafted by malicious tiny noise, which is imperceptible to human observers but can make DNNs output the wrong result. Existing adversarial attacks can be categorized into digital and physical adversarial attacks. The former is designed to pursue strong attack performance in lab environments while hardly remaining effective when applied to the physical world. In contrast, the latter focus on developing physical deployable attacks, thus exhibiting more robustness in complex physical environmental conditions. Recently, with the increasing deployment of the DNN-based system in the real world, strengthening the robustness of these systems is an emergency, while exploring physical adversarial attacks exhaustively is the precondition. To this end, this paper reviews the evolution of physical adversarial attacks against DNN-based computer vision tasks, expecting to provide beneficial information for developing stronger physical adversarial attacks. Specifically, we first proposed a taxonomy to categorize the current physical adversarial attacks and grouped them. Then, we discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions. Finally, we discuss the issues of the current physical adversarial attacks to be solved and give promising directions