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    Unsupervised obstacle detection in driving environments using deep-learning-based stereovision

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    1-s2.0-S0921889017304736-main.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Dairi, Abdelkader
    Harrou, Fouzi cc
    Senouci, Mohamed
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2017-12-06
    Online Publication Date
    2017-12-06
    Print Publication Date
    2018-02
    Permanent link to this record
    http://hdl.handle.net/10754/626385
    
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    Abstract
    A 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.
    Citation
    Dairi 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.
    Sponsors
    The 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.
    Publisher
    Elsevier BV
    Journal
    Robotics and Autonomous Systems
    DOI
    10.1016/j.robot.2017.11.014
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S0921889017304736
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.robot.2017.11.014
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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