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Die Prognose über den Studienerfolg von Studierenden in MINT-Fächern wird häufig an ihre Prüfungsleistungen in mathematischen und naturwissenschaftlichen Veranstaltungen geknüpft. Die Relevanz von sprachlichen Kompetenzen, insbesondere der Textrezeption und -produktion, für Studium und Berufstätigkeit, gerade auch in ingenieurwissenschaftlichen Betätigungsfeldern, wird sowohl von den Studierenden als auch von Dozent*innen nicht erkannt oder zurückgestellt.
Trends of environmental awareness, combined with a focus on personal fitness and health, motivate many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities and communities need to know how many micromobility vehicles are on the road. In a previous work, we proposed a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles utilizing uncooled long-wave infrared (LWIR) image sensors and a neuromorphic co-processor. In this work, we elaborate on this concept by focusing on the feature extraction process with the goal to increase the classification accuracy. We demonstrate that even with a reduced feature list compared with our early concept, we manage to increase the detection precision to more than 90%. This is achieved by reducing the images of 160 × 120 pixels to only 12 × 18 pixels and combining them with contour moments to a feature vector of only 247 bytes.
