1 code implementation • 25 Apr 2022 • Lukas Ewecker, Lars Ohnemus, Robin Schwager, Stefan Roos, Tim Brühl, Sascha Saralajew
We show that this approach allows for an automated derivation of different object representations, such as binary maps or bounding boxes so that detection models can be trained on different annotation variants and the problem of providently detecting vehicles at night can be tackled from different perspectives.
no code implementations • 23 Jul 2021 • Lukas Ewecker, Ebubekir Asan, Lars Ohnemus, Sascha Saralajew
To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively.
1 code implementation • 27 May 2021 • Sascha Saralajew, Lars Ohnemus, Lukas Ewecker, Ebubekir Asan, Simon Isele, Stefan Roos
In this paper, we study the problem of how to map this intuitive human behavior to computer vision algorithms to detect oncoming vehicles at night just from the light reflections they cause by their headlights.
1 code implementation • 31 Dec 2020 • Lars Ohnemus, Lukas Ewecker, Ebubekir Asan, Stefan Roos, Simon Isele, Jakob Ketterer, Leopold Müller, Sascha Saralajew
As humans, we intuitively assume oncoming vehicles before the vehicles are actually physically visible by detecting light reflections caused by their headlamps.