In recent years, the use of video conferencing has been extended to games and remote
experiments, both of which rely on computer vision algorithms. However, it has been
shown that these algorithms can be negatively impacted by the compression necessary
for real-time video. To increase the robustness against this issue, a semantic encoding
pipeline was built that is based on H.264 and works in combination with WebRTC.
Two ROI detection methods based on saliency and face detection, were tested using
six videos at two different bitrates (750 and 1500 kbit/s). To evaluate these methods,
the resulting encoded videos were transmitted via WebRTC and analyzed using an
rPPG and an AU extraction algorithm. The results showed that our semantic encoding
methods improve accuracy while only decreasing encoding speed by a small amount.
These findings show that such a pipeline is technically viable and, in the future, can
help to make games more robust and experiment results more trustable.

Open-world games are defined through their freedom in spatial and narrative terms. However, this freedom makes it difficult to guide players through the game and provide the right amount of choice so the player is neither frustrated nor overwhelmed. This poses a design challenge for game developers. While various solutions for this challenge have been identified, they are usually limited to one application area or do not describe the use and consequences of these solutions. This thesis identifies a list of patterns for multiple application areas. It describes the patterns through their use in published open-world games and discusses their application and consequences. Game developers can apply these patterns to solve the challenge of guiding players through open-world games.

The goal of this bachelors thesis is to create a maze based Serious Game connected to the Ancient Greek language and culture. The Serious Game "Labyrinthos" centers on high replayability and exploration, utilizing the natural curiosity of humans. This thesis tries to answer the key questions of, what requirements should be met to create an effective maze based Serious Game and to what extend this game can motivate players to learn and experience an ancient language and culture. In order to accomplish this, important fundamentals of Serious Games were determined and implemented into the game. As this project is closely tied to language learning, it is also essential to include key aspects of it. This is achieved by researching related work tied to both cultural and linguistic topics, and merge their findings to gain an overview over the most important features. Consequently the game was evaluated and further improvements were derived from user feedback.

Systems for automated driving are getting more advanced over the years, and necessarily the developers of the systems need to ensure their functionality. Sensor data visualization is used during development to gather knowledge of car behavior and sensors while testing. This thesis investigates the current state-of-the-art sensor data visualization platforms and explores users’ needs and expectations of these systems by conducting a pre-study identifying improvements. Following a low-fidelity clickable prototype design process with the knowledge collected, proposing a new interface that covers in-car and local usage and provides a structured and intuitive surface. The prototype is evaluated with usability tests and final developer interviews, gathering primarily positive feedback and improvements regarding the design and functionality. A few requests were refined and implemented, but there are still some expectations for future work, such as optimizing the ideas of the prototype and further research in this domain.