Technological advances enable the development of new low vision aids. One such new technology is virtual reality (VR). Existing VR applications offer a variety of adjustments to the content and the user's environment to support people with low vision. Yet, an interaction concept to conveniently activate and control these aids is missing. Therefore, we designed and implemented an interaction concept based on user and expert feedback and evaluated it in a user study. Our application offers various aids to support residual vision and a radial menu for intuitive use of these aids. The results of the user study show that the VR application allows simple task solving comparable to a desktop computer. Furthermore, the user study gives an insight into the effects of different visual impairments on the usage of VR, since the field of view has a larger impact than visual acuity. Overall, our results indicate that VR is a suitable aid to support the individual needs of users with low vision.

The importance of remote collaboration grows in an interconnected world as the reasons to avoid travel increase. The spatial rendering and collaboration capabilities of virtual and augmented reality systems are well suited for tasks such as support or training. Users can take a shared perspective to build a common understanding. Also, users may engage in face-to-face cooperation to support interpersonal communication. However, a shared perspective and face-to-face collaboration are both desirable but naturally exclude each other. We place all users at the same location to provide a shared perspective. To avoid overlapping body parts, the avatars of the other connected users are shifted to the side. A redirected body pose modification corrects the resulting inconsistencies. The implemented system is compared to a baseline of two users standing in the same location and working with overlapping avatars. The results of a user study show that the proposed modifications provide an easy to use, efficient collaboration and yield higher co-presence and the feeling of teamwork. Applying redirection techniques to other users opens up novel ways to increase social presence for local or remote collaboration.

Driving in stressful and frustrating situations remains a common issue in daily traffic scenarios and has been shown to increase the risk for hazardous and aggressive driving style. Aiming to improve road safety with intelligent systems, frustration has to be detected continuously as well as robust and mitigation strategies must be applied effectively. Since both, the modeling of frustration over time as well as the design and timing of applications for frustration mitigation, are complex tasks, we divided this work in two parts: (1) A driving simulator experiment was conducted to collect a dataset and to validate a driving context related frustration induction method. With this dataset we developed a bimodal frustration detection for the driving context using a temporal support vector machine. The detection combines drivers' visual facial features with heart rate measurements and yields an accuracy of 88.7% (AUC of ROC). (2) We applied the real-time frustration detection in a second simulator study to evaluate two application scenarios for frustration mitigation, which include a frustration sensitive ambient light and an autonomous driving assistant. Both applications were examined for their frustration mitigating effect as well as in terms of user experience. The results provide a helpful basis to develop future intelligent frustration mitigation systems.

Immersive output and effortless input are two core aspects of a virtual reality (VR) experience. We transfer ubiquitous touch interaction with haptic feedback into a virtual environment (VE). The movable and cheap real world object supplies an accurate touch detection equal to a ray-casting interaction with a controller. Moreover, the virtual tablet extends the functionality of a real world tablet. Additional information is displayed in mid-air around the touchable area and the tablet can be turned over to interact with both sides. It allows easy to learn and precise system interaction and can even augment the established touch metaphor with new paradigms.

Virtual Reality opens up new possibilities as it allows to overcome real-life limitations and create novel experiences. While interacting with other people, it is beneficial to share a common view point. We modify the virtual world to allow face-to-face interaction with another person, while still retaining an optimal point of view on presented data. This is done by adapting the virtual environment independently for each user, using translation, rotation and scaling. The presented modification of the world gives a natural solution to the problems of collaborative analysis of content. It is therefore beneficial for usage in human-human interaction scenarios that support cooperative work.

The large variety of influencing factors requires the manual creation and optimization of person trackers for different environments as well as different views, such as in a distributed network of cameras. The manual creation and adjustments are time-consuming as well as prone to error and therefore expensive. We propose a system that uses basic computer-vision building blocks to automatically create and optimize a person tracker, using only a few annotated camera frames. An evaluation shows that our system creates target-oriented trackers from largely very basic methods, that can compare to manually, published person trackers. The system has the potential to drastically reduce the cost of creating person trackers for new environments and optimizing it for every single camera in a camera network.

Head pose estimation is an important part of the human perception and is therefore also relevant to make interaction with computer systems more natural. However, accurate estimation of the pose in a wide range is a challenging computer vision problem. We present an accurate approach for head pose estimation on consumer depth cameras that works in a wide pose range without prior knowledge about the tracked person and without prior training of a detector. Our algorithm builds and registers a 3D head model with the iterative closest point algorithm. To track the head pose using this head model an initialization with a known pose is necessary. Instead of providing such an initialization manually we determine the initial pose using features of the head and improve this pose over time. An evaluation shows that our algorithm works in real time with limited resources and achieves superior accuracy compared to other state of the art systems. Our main contribution is the combination of features of the head and the head model generation to build a detector that gives accurate results in a wide pose range.

Within a manufacturing system, a smart human-machine interface reduces the chances of human error and helps users to make informed decisions, especially in critical situations. This paper presents a concept for a flexible context specific assistance system for industrial applications using camera based localization. As a central element, a modular and service oriented context aware system aggregates relevant data from different sources. An object recognition service applies image analysis techniques on a video stream, captured from a top mounted multi-camera system to detect a person's location and associate it with a mobile device. A semantically enriched OPC UA server provides access to process data, and a web-service provides connection to a user interface hosted on a tablet PC. A case study provides an application of the proposed solution for the maintenance support and indoor navigation is implemented as a proof of concept.

As new input modalities allow interaction not only in front of a single display, but enable interaction in the whole room, application developers face new challenges. They have to handle many new input modalities, each with its own interface and requirements for pre-processing, deal with multiple displays, and applications that are distributed across multiple machines. We present glueTK, a framework that abstracts from the complexities of these input modalities, allows the design of interfaces for a wide range of display sizes, and makes the distribution across multiple machines transparent to the developer as well as the user. With an example application we demonstrate the wide range of input modalities glueTK can support and the functionality this enables. GlueTK moves away from the focus on point and touch like input modalities, enabling the design of applications tailored towards interactive rooms instead of the traditional desktop environment.

This paper presents an automatic system for the monitoring of indoor environments using pan-tilt-zoomable cameras. A combination of Haar-feature classifier-based detection and color histogram filtering is used to achieve reliable initialization of person tracks even in the presence of camera movement. A combination of adaptive color and KLT feature trackers for face and upper body allows for robust tracking and track recovery in the presence of occlusion or interference. The continuous recomputation of camera parameters, coupled with a fuzzy controlling scheme allow for smooth tracking of moving targets as well as acquisition of stable facial close ups, similar to the natural behavior of a human cameraman. The system is tested on a series of natural indoor monitoring scenarios and shows a high degree of naturalness, flexibility and robustness.