Human Computer Interaction

Human-Computer Interaction

Eye-tracking technology has found its way into many fields of research and applications, enabling sensing of the visual behavior in mobile, outdoor settings. Our vision are computing systems that sense and infer the user’s cognitive state based on eye movements and provide information for assistive technologies in many activities of everyday life.

Towards this vision, our research has focused on (i) the development of novel algorithms and tools for real-time and efficient eye tracking and data analysis and (ii) modelling user behavior based on eye movement analysis. We provide a comprehensive tool-chain of methods, starting from robust gaze estimation in natural settings through event detection to the automated analysis of visual exploration. Based on these methods, we have explored visual information processing, visual search and cognitive aspects related to vision in laboratory, virtual reality and real-world settings, such as driving or shopping.

Recent News


Paper acceptance at the IEEE International Joint Conference on Neural Networks (IJCNN).

Simple image rotations significantly reduce the accuracy of deep neural networks. Moreover, training with all possible rotations increases the data set, which also increases the training duration. In this work, we address trainable rotation invariant convolutions as well as the construction of nets, since fully connected layers can only be rotation invariant with a one-dimensional input. On the one hand, we show that our approach is rotationally invariant for different models and on different public data sets. We also discuss the influence of purely rotational invariant features on accuracy. The rotationally adaptive convolution models presented in this work are more computationally intensive than normal convolution models. Therefore, we also present a depth wise separable approach with radial convolution.

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Two paper accepted at the ACM Conference on Eye Tracking Research & Applications (ETRA).

The paper “55 Rides: attention annotated head and gaze data during naturalistic driving” as well as the paper “A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis” was accepted at ACM Conference on Eye Tracking Research & Applications (ETRA).

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Paper acceptance at the IEEE Conference on Virtual Reality and 3D User Interfaces 2021 (IEEE VR 2021).

The paper “Exploiting Object-of-Interest Information to Understand Attention in VR Classrooms” was accepted at the IEEE VR 2021. Details will follow.

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