Track-based self-supervised classification of dynamic obstacles
This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that relies on the stability of a subset of features given by a sensor to perform an initial robust classification based on unsupervise...
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| Published in: | Autonomous robots Vol. 29; no. 2; pp. 219 - 233 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01-08-2010
Springer Nature B.V |
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| Abstract | This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that relies on the stability of a subset of features given by a sensor to perform an initial robust classification based on unsupervised techniques. The obtained results are used as labels to train a set of supervised classifiers. The outcomes obtained with the second sensor can be used for higher level tasks such as segmentation or to refine the within-clusters discrimination. The proposed architecture is evaluated for a particular realization based on range and visual information which produces track-based labeling that is then employed to train supervised modules that perform instantaneous classification. Experiments show that the system is able to achieve 95% classification accuracy and to maintain the performance through on-line retraining when working conditions change. |
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| AbstractList | This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that relies on the stability of a subset of features given by a sensor to perform an initial robust classification based on unsupervised techniques. The obtained results are used as labels to train a set of supervised classifiers. The outcomes obtained with the second sensor can be used for higher level tasks such as segmentation or to refine the within-clusters discrimination. The proposed architecture is evaluated for a particular realization based on range and visual information which produces track-based labeling that is then employed to train supervised modules that perform instantaneous classification. Experiments show that the system is able to achieve 95% classification accuracy and to maintain the performance through on-line retraining when working conditions change. |
| Author | Douillard, Bertrand Nieto, Juan Nebot, Eduardo Katz, Roman |
| Author_xml | – sequence: 1 givenname: Roman surname: Katz fullname: Katz, Roman email: r.katz@acfr.usyd.edu.au organization: ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, The University of Sydney – sequence: 2 givenname: Juan surname: Nieto fullname: Nieto, Juan organization: ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, The University of Sydney – sequence: 3 givenname: Eduardo surname: Nebot fullname: Nebot, Eduardo organization: ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, The University of Sydney – sequence: 4 givenname: Bertrand surname: Douillard fullname: Douillard, Bertrand organization: ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, The University of Sydney |
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| Cites_doi | 10.1109/ICIP.1995.537667 10.1145/1282280.1282340 10.1109/TPAMI.2006.104 10.1023/B:VISI.0000029664.99615.94 10.5244/C.16.36 10.1016/S0031-3203(00)00146-1 10.1109/CVPR.2000.854754 10.1214/aos/1016218223 10.1109/TPAMI.1986.4767851 10.1109/34.121791 10.1109/ROBOT.1985.1087316 10.1214/ss/1009212519 10.1023/B:VISI.0000013087.49260.fb 10.1109/IROS.2008.4650636 10.1109/IROS.2003.1249281 10.1016/B978-0-08-051584-7.50027-9 10.1109/ICCV.2003.1238354 |
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| Copyright | Springer Science+Business Media, LLC 2010 Autonomous Robots is a copyright of Springer, (2010). All Rights Reserved. |
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| Keywords | Learning and adaptive systems Intelligent vehicles Obstacle classification Self-supervised learning |
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| Snippet | This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical... |
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| SubjectTerms | Architecture Artificial Intelligence Autonomous Classification Computer Imaging Control Engineering Labels Mechatronics Moving obstacles Obstacles Pattern Recognition and Graphics Retraining Robotics Robotics and Automation Robots Segmentation Sensors Trains Urban environments Vision |
| Title | Track-based self-supervised classification of dynamic obstacles |
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