Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107732
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Type: | Conference paper |
Title: | A fast, modular scene understanding system using context-aware object detection |
Author: | Cadena, C. Dick, A. Reid, I. |
Citation: | IEEE International Conference on Robotics and Automation, 2015, vol.2015-June, iss.June, pp.4859-4866 |
Publisher: | IEEE |
Issue Date: | 2015 |
Series/Report no.: | IEEE International Conference on Robotics and Automation ICRA |
ISBN: | 9781479969234 |
ISSN: | 1050-4729 2577-087X |
Conference Name: | 2015 IEEE International Conference on Robotics and Automation (ICRA 2015) (26 May 2015 - 30 May 2015 : Seattle, WA) |
Statement of Responsibility: | Cesar Cadena, Anthony Dick and Ian D. Reid |
Abstract: | We propose a semantic scene understanding system that is suitable for real robotic operations. The system solves different tasks (semantic segmentation and object detections) in an opportunistic and distributed fashion but still allows communication between modules to improve their respective performances. We propose the use of the semantic space to improve specific out-of-the-box object detectors and an update model to take the evidence from different detection into account in the semantic segmentation process. Our proposal is evaluated with the KITTI dataset, on the object detection benchmark and on five different sequences manually annotated for the semantic segmentation task, demonstrating the efficacy of our approach. |
Keywords: | Semantics, detectors, context, benchmark testing, training, robots, object detection |
Rights: | © 2015 IEEE |
DOI: | 10.1109/ICRA.2015.7139874 |
Grant ID: | http://purl.org/au-research/grants/arc/DP130104413 http://purl.org/au-research/grants/arc/CE140100016 http://purl.org/au-research/grants/arc/FL130100102 |
Published version: | http://dx.doi.org/10.1109/icra.2015.7139874 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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RA_hdl_107732.pdf Restricted Access | Restricted Access | 2.72 MB | Adobe PDF | View/Open |
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