Paper Info
Title
Fast Image-Based Obstacle Detection From Unmanned Surface Vehicles.
Abstract
Obstacle detection plays an important role in unmanned surface vehicles (USVs). The USVs operate in a highly diverse environments in which an obstacle may be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline, which presents a significant challenge to continuous detection from images taken on board. This paper addresses the problem of online detection by constrained, unsupervised segmentation. To this end, a new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured on board a USV. The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints. A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived. Our approach does not require computationally intensive extraction of texture features and comfortably runs in real time. The algorithm is tested on a new, challenging, dataset for segmentation, and obstacle detection in marine environments, which is the largest annotated dataset of its kind. Results on this dataset show that our model outperforms the related approaches, while requiring a fraction of computational effort.
Year
DOI
Venue
2015
10.1109/TCYB.2015.2412251
IEEE transactions on cybernetics
Keywords
Field
DocType
autonomous surface vehicles,gaussian mixture models,markov random fields (mrfs),obstacle-map estimation.
Obstacle,Computer vision,Scale-space segmentation,Segmentation,Markov random field,Image based,Image segmentation,Artificial intelligence,Graphical model,Simultaneous optimization,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
PP
99
2168-2275
Citations 
PageRank 
References 
9
0.58
35
Authors
4
Name
Order
Citations
PageRank
Matej Kristan196047.02
Vildana Sulíc Kenk290.58
Stanislav Kovacic39110.68
Janez Pers426519.24