TY - JOUR
T1 - Unsupervised obstacle detection in driving environments using deep-learning-based stereovision
AU - Dairi, Abdelkader
AU - Harrou, Fouzi
AU - Senouci, Mohamed
AU - Sun, Ying
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: 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.
PY - 2017/12/6
Y1 - 2017/12/6
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/626385
UR - http://www.sciencedirect.com/science/article/pii/S0921889017304736
UR - http://www.scopus.com/inward/record.url?scp=85041286281&partnerID=8YFLogxK
U2 - 10.1016/j.robot.2017.11.014
DO - 10.1016/j.robot.2017.11.014
M3 - Article
SN - 0921-8890
VL - 100
SP - 287
EP - 301
JO - Robotics and Autonomous Systems
JF - Robotics and Autonomous Systems
ER -