Can I read Fast Image-based Obstacle Detection from Unmanned Surface Vehicles on EtoBox?

Fast Image-based Obstacle Detection from Unmanned Surface Vehicles by Kristan, Matej; Sulic, Vildana; Kovacic, Stanislav; Pers, Janez is a scholarly article available to read on EtoBox.

What is Fast Image-based Obstacle Detection from Unmanned Surface Vehicles about?

Obstacle detection plays an important role in unmanned surface vehicles (USV). The USVs operate in 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 onboard. 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 onboard 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

Author

Kristan, Matej; Sulic, Vildana; Kovacic, Stanislav; Pers, Janez

Published

2015

Language

EN