S
Sebastian Ramos
Researcher at Autonomous University of Barcelona
Publications - 19
Citations - 12151
Sebastian Ramos is an academic researcher from Autonomous University of Barcelona. The author has contributed to research in topics: Object detection & Pedestrian detection. The author has an hindex of 14, co-authored 19 publications receiving 7829 citations. Previous affiliations of Sebastian Ramos include University of São Paulo & Daimler AG.
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Proceedings ArticleDOI
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
TL;DR: This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.
Posted Content
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
TL;DR: Cityscapes as discussed by the authors is a large-scale dataset for semantic urban scene understanding, consisting of 5000 images with high quality pixel-level annotations and 200,000 additional images with coarse annotations.
Proceedings Article
The Cityscapes Dataset
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Scharwächter,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
TL;DR: This paper aims to provide a large and diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations in addition to a larger set of weakly annotated frames, an order of magnitude larger than similar previous attempts.
Proceedings ArticleDOI
Vision-Based Offline-Online Perception Paradigm for Autonomous Driving
TL;DR: This paper challenges state-of-the-art computer vision algorithms for building a perception system for autonomous driving by following an offline-online strategy that retrieves accurate semantics and 3D geometry in real-time.
Proceedings ArticleDOI
Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling
TL;DR: In this article, a new deep learning-based obstacle detection framework was proposed to detect small road hazards, such as lost cargo, in self-driving cars, which leverages appearance, contextual as well as geometric cues.