Efficient Multi-cue Scene Segmentation

By Timo Scharwächter, Markus Enzweiler, Uwe Franke, Stefan Roth. 2013

95 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

The Cityscapes Dataset for Semantic Urban Scene Understanding (2016, background): “Also in this area, research progress can be heavily linked to the existence of datasets such as the KITTI Vision Benchmark Suite [19], CamVid [7], Leuven [35], and Daimler Urban Segmentation [61] datasets.”
Survey on semantic segmentation using deep learning techniques (2019, background): “…automotive datasets includes; CamVid dataset [180] which is considered as the ﬁrst with semantically annotated videos, Daimler Urban Segmentation [181], CityScapes [182], Mapillary Vistas [183] and the most recent Apolloscape-Scene parsing [184] which focuses on semantic understanding of urban…”
UAVid: A semantic segmentation dataset for UAV imagery (2018, background): “There are more semantic segmentation datasets designed using street scenes for autonomous driving scenarios [4, 20, 12, 32, 38, 18].”
The Cityscapes Dataset (2015, methodology): “Furthermore, the task of a semantic understanding of urban scenarios is addressed by several datasets, such as CamVid [1], Leuven [13], and Daimler Urban Segmentation [21].”
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes (2016, background): “For instance, for some ADAS and for AD, semantic segmentation is a key issue [33, 18, 29] and it requires pixel-level annotations (i.”
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments (2018, methodology): “The datasets proposed for semantic segmentation that focus on autonomous navigation are Cityscapes [5], KITTI [15], Camvid [2, 1], Leuven [12] and the Daimler Dataset [21].”
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes (2017, methodology): “To gain deeper understanding of the complex interactions between e.g. traffic participants in street-level images, a lot of research efforts and investments have recently gone into designing and creating datasets which were used to train deep models for this specific task, such as CamVid [3], the KI”
MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes (2017, background): “Public image segmentation datasets built for autonomous driving, such as CamVid [13], Cityscapes [14], and Daimler Urban Segmentation [15], are based on visible spectral (or”
Stixmantics: A Medium-Level Model for Real-Time Semantic Scene Understanding (2014, methodology): “Most closely related to this work is [36], which also combines Stixels and a bag-of-features classification scheme for semantic segmentation.”