Open AccessFusion‐based holistic road scene understanding
Author(s) -
Huang Wenqi,
Zhang Fuzheng,
Xu Aidong,
Chen Huajun,
Li Peng
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8319
Subject(s) - computer science , conditional random field , segmentation , artificial intelligence , prior probability , cluster analysis , object (grammar) , graph , range (aeronautics) , visual reasoning , cognitive neuroscience of visual object recognition , field (mathematics) , machine learning , pattern recognition (psychology) , theoretical computer science , mathematics , bayesian probability , materials science , pure mathematics , composite material
This study addresses the problem of holistic road scene understanding based on the integration of visual and range data. To achieve the grand goal, the authors propose an approach that jointly tackles object‐level image segmentation and semantic region labelling within a conditional random field (CRF) framework. Specifically, the authors first generate semantic object hypotheses by clustering 3D points, learning their prior appearance models, and using a deep learning method for reasoning their semantic categories. The learned priors, together with spatial and geometric contexts, are incorporated in CRF. With this formulation, visual and range data are fused thoroughly, and moreover, the coupled segmentation and semantic labelling problem can be inferred via graph cuts. The authors’ approach is validated on the challenging KITTI dataset that contains diverse complicated road scenarios. Both quantitative and qualitative evaluations demonstrate its effectiveness .