Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Alina Roitberg"'
Autor:
Alina Roitberg, Kunyu Peng, David Schneider, Kailun Yang, Marios Koulakis, Manuel Martinez, Rainer Stiefelhagen
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:25271-25286
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1bf37104bb9aaf84ffa33e34c77232b1
https://doi.org/10.1109/tits.2022.3196410
https://doi.org/10.1109/tits.2022.3196410
Autor:
Christian R. G. Dreher, Manuel Zaremski, Fabian Leven, David Schneider, Alina Roitberg, Rainer Stiefelhagen, Michael Heizmann, Barbara Deml, Tamim Asfour
Publikováno v:
at - Automatisierungstechnik. 70:517-533
Zusammenfassung Der Mensch ist die flexibelste, aber auch eine teure Ressource in einem Produktionssystem. Im Kontext des Remanufacturings sind Roboter eine kostengünstige Alternative, jedoch ist deren Programmierung oft nicht rentabel. Das Programm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b55675fbbbbc9eab412f7a14336dc94
https://doi.org/10.1515/auto-2022-0006
https://doi.org/10.1515/auto-2022-0006
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250842
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35e0d6e9074f40249cda95fd5ce8f36b
https://doi.org/10.1007/978-3-031-25085-9_19
https://doi.org/10.1007/978-3-031-25085-9_19
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::557723638fc88a521b8a970deba5c20f
https://doi.org/10.1109/cvprw56347.2022.00387
https://doi.org/10.1109/cvprw56347.2022.00387
Autor:
Alina Roitberg, Kunyu Peng, Zdravko Marinov, Constantin Seibold, David Schneider, Rainer Stiefelhagen
Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-vehicle interaction but such systems face substantial obstacles as they need to capture different granularities of driver behaviour while dealing with highly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82da1e8f4440f9dc6247311b5bce443e
http://arxiv.org/abs/2204.04734
http://arxiv.org/abs/2204.04734
Occlusions are universal disruptions constantly present in the real world. Especially for sparse representations, such as human skeletons, a few occluded points might destroy the geometrical and temporal continuity critically affecting the results. Y
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ba5215f2619e75cbb0f4c68945719ac
Autonomous vehicles clearly benefit from the expanded Field of View (FoV) of 360-degree sensors, but modern semantic segmentation approaches rely heavily on annotated training data which is rarely available for panoramic images. We look at this probl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1963e3ea9fc069dad14bd2b9c48e4c02
http://arxiv.org/abs/2110.11062
http://arxiv.org/abs/2110.11062
Publikováno v:
ITSC
Intelligent vehicles clearly benefit from the expanded Field of View (FoV) of the 360-degree sensors, but the vast majority of available semantic segmentation training images are captured with pinhole cameras. In this work, we look at this problem th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e40e3708f2857a53040c6102234f57a
https://doi.org/10.1109/itsc48978.2021.9564920
https://doi.org/10.1109/itsc48978.2021.9564920
From Driver Talk To Future Action: Vehicle Maneuver Prediction by Learning from Driving Exam Dialogs
Publikováno v:
IV
A rapidly growing amount of content posted online inherently holds knowledge about concepts of interest, i.e. driver actions. We leverage methods at the intersection of vision and language to surpass costly annotation and present the first automated
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5144fb7eee4b5356a8d0812ebe87a232
https://doi.org/10.1109/iv48863.2021.9575655
https://doi.org/10.1109/iv48863.2021.9575655
Autor:
Kunyu Peng, Juncong Fei, Kailun Yang, Alina Roitberg, Jiaming Zhang, Frank Bieder, Philipp Heidenreich, Christoph Stiller, Rainer Stiefelhagen
At the heart of all automated driving systems is the ability to sense the surroundings, e.g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progress due to the release of large datasets such as SemanticKITTI and nuS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b06a7e7b2778af846364bca44efdb017
http://arxiv.org/abs/2107.00346
http://arxiv.org/abs/2107.00346