Show simple item record

dc.contributor.authorDairi, Abdelkader
dc.contributor.authorHarrou, Fouzi
dc.contributor.authorSenouci, Mohamed
dc.contributor.authorSun, Ying
dc.date.accessioned2017-12-14T12:34:05Z
dc.date.available2017-12-14T12:34:05Z
dc.date.issued2017-12-06
dc.identifier.citationDairi A, Harrou F, Senouci M, Sun Y (2017) Unsupervised obstacle detection in driving environments using deep-learning-based stereovision. Robotics and Autonomous Systems. Available: http://dx.doi.org/10.1016/j.robot.2017.11.014.
dc.identifier.issn0921-8890
dc.identifier.doi10.1016/j.robot.2017.11.014
dc.identifier.urihttp://hdl.handle.net/10754/626385
dc.description.abstractA vision-based obstacle detection system is a key enabler for the development of autonomous robots and vehicles and intelligent transportation systems. This paper addresses the problem of urban scene monitoring and tracking of obstacles based on unsupervised, deep-learning approaches. Here, we design an innovative hybrid encoder that integrates deep Boltzmann machines (DBM) and auto-encoders (AE). This hybrid auto-encode (HAE) model combines the greedy learning features of DBM with the dimensionality reduction capacity of AE to accurately and reliably detect the presence of obstacles. We combine the proposed hybrid model with the one-class support vector machines (OCSVM) to visually monitor an urban scene. We also propose an efficient approach to estimating obstacles location and track their positions via scene densities. Specifically, we address obstacle detection as an anomaly detection problem. If an obstacle is detected by the OCSVM algorithm, then localization and tracking algorithm is executed. We validated the effectiveness of our approach by using experimental data from two publicly available dataset, the Malaga stereovision urban dataset (MSVUD) and the Daimler urban segmentation dataset (DUSD). Results show the capacity of the proposed approach to reliably detect obstacles.
dc.description.sponsorshipThe authors (Abdelkader Dairi and Mohamed Senouci) would like to thank the Computer Science Department, University of Oran 1 Ahmed Ben Bella for the continued support during the research. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors would like to thank two anonymous referees whose comments and suggestions have improved the content and presentation of this work.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0921889017304736
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Robotics and Autonomous Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Autonomous Systems, 6 December 2017. DOI: 10.1016/j.robot.2017.11.014. © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDeep learning
dc.subjectDBM
dc.subjectAutoencoder
dc.subjectOCSVM
dc.subjectMonitoring
dc.subjectStereovision
dc.titleUnsupervised obstacle detection in driving environments using deep-learning-based stereovision
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalRobotics and Autonomous Systems
dc.eprint.versionPost-print
dc.contributor.institutionComputer Science Department, University of Oran 1 Ahmed Ben Bella, Algeria Street El senia el mnouer bp 31000 Oran, Algeria
kaust.personHarrou, Fouzi
kaust.personSun, Ying
kaust.grant.numberOSR-2015-CRG4-2582
refterms.dateFOA2019-12-06T00:00:00Z
dc.date.published-online2017-12-06
dc.date.published-print2018-02


Files in this item

Thumbnail
Name:
1-s2.0-S0921889017304736-main.pdf
Size:
9.114Mb
Format:
PDF
Description:
Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record