Improvement of Symbiotic Attention through Privileged Information from Gaze Estimation for Egocentric Action Recognition

January 1, 2020 chair/team/bjoern-severittchair/team/thomas-kueblerMy research concerns all aspects of eye-tracking, from the design of a physical recording device, the necessary image processing steps via classical computer vision as well as DNNs, to the high-level interpretation of recorded gaze sequences through machine learning. I work on assessing data quality, visualization and gaze analysis tools in various applications in the medical, educational, automotive and art historian fields. With my Spin-Off Look! we develop eye-tracking solutions for automotive applications as well as in-vehicle head-mounted devices for driving schools and instructor teaching. Our algorithms for the registration and analysis of gaze data are well suited to infer cognitive processes such as attention and vigilance solely from the movement of the eyes - and are therefore an excellent complementary factor to traditional measures such as perclos, head movements or gaze-on-road. This adds to robustness and sensitivity of driver monitoring systems by enabling the vehicle to sense the current attentional state of the driver. Driving instructors are enabled by our head-mounted eye-tracking devices to see the streets through the eyes of their students. That way they can provide efficient feedback and speed up the learning process by making students aware of the importance of correct visual exploration of their surroundings. {% aside %}

{% endaside %} {% box %} ## Research Interests * Gaze-based driver assistance and monitoring systems * Computational models of human gaze behavior * Algorithms for the comparison of exploratory gaze sequences * Eye-tracking data quality in real-world applications * Algorithms and tools for the analysis and visualization of eye-tracking data * Head-mounted eye-tracking hardware {% endbox %}