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Nandan, S.* ; Karthik, S. ; Georgescu, M.-I. ; Caputo, B.* ; Masone, C.* ; Akata, Z.

Road Obstacle Video Segmentation.

In: (Pattern Recognition). Berlin [u.a.]: Springer, 2026. 186 - 201 (Lect. Notes Comput. Sc. ; 16125 LNCS)
DOI
With the growing deployment of autonomous driving agents, the detection and segmentation of road obstacles have become critical to ensure safe autonomous navigation. However, existing road-obstacle segmentation methods are applied on individual frames, overlooking the temporal nature of the problem, leading to inconsistent prediction maps between consecutive frames. In this work, we demonstrate that the road-obstacle segmentation task is inherently temporal, since the segmentation maps for consecutive frames are strongly correlated. To address this, we curate and adapt four evaluation benchmarks for road-obstacle video segmentation and evaluate 11 state-of-the-art image- and video-based segmentation methods on these benchmarks. Moreover, we introduce two strong baseline methods based on vision foundation models. Our approach establishes a new state-of-the-art in road-obstacle video segmentation for long-range video sequences, providing valuable insights and direction for future research.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Obstacle Detection ; Video Segmentation
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Pattern Recognition
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 16125 LNCS, Heft: , Seiten: 186 - 201 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)