Domestic service robots are becoming increasingly available and affordable. However, interaction with the real world is still a challenge. Tracker systems are fast and can yield good results because they exploit temporal coherence, but often need manual initialization. In this research such a tracker system was combined with a detector system that initializes and corrects the tracker automatically. The system was used to track faces in a dataset. Different (combinations of) features were explored. The combined system was also used to guide a mobile, autonomous robot. Data was gathered as the robot performed its task. This data was used to train the tracker system, to make it more robust to changes in the environment, like changing illumination conditions. This robustness is necessary for the tracking system to handle the changing conditions in the real world without a lot of feedback from an external system. The combined detection and tracking system was compared to the detector system is isolation. The combined system was able to run faster or perform better, depending on its settings. Adaptive combination rules helped to choose the best feature(s) for a certain situation. In an evaluation on data gathered in the real world, the trained tracker performed better than the non-learning tracker.
The University of Groningen Faculty of Science and Engineering has won the very first NNV Diversity Award. The Netherlands Physics Association (NNV) has established the award for physics institutions that best put into practice an open diversity policy...
Two promising UG academics, Dr Michael Lerch and Sanne van Dijk, will be able to conduct research at top institutes abroad for two years thanks to the Rubicon programme organized by the Netherlands Organisation for Scientific Research (NWO).
His opponent was fourfold world champion Alexander Schwarzman. Boomstra, who studies Physics at the University of Groningen, also won the title in 2016.