The Cityscapes Dataset for Semantic Urban Scene Understanding

04/06/2016 ∙ by Marius Cordts, et al. ∙ 0

Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 15

page 16

page 21

page 25

page 26

page 27

page 28

page 29

Code Repositories

SMSnet

Model-evaluator of Publication : "SMSnet: Semantic Motion Segmentation using Deep Convolutional Neural Networks"


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.