Domain adaptation for vehicle detection from bird's eye view lidar point cloud data

Saleh, K; Abobakr, A; Attia, M; Iskander, J; Nahavandi, D; Hossny, M; Nahvandi, S

Domain adaptation for vehicle detection from bird's eye view lidar point cloud data

Saleh, K Abobakr, A Attia, M Iskander, J Nahavandi, D Hossny, M Nahvandi, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2020, 00, pp. 3235-3242
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Saleh, K
dc.contributor.author	Abobakr, A
dc.contributor.author	Attia, M
dc.contributor.author	Iskander, J
dc.contributor.author	Nahavandi, D
dc.contributor.author	Hossny, M
dc.contributor.author	Nahvandi, S
dc.date	2019-10-27
dc.date.accessioned	2021-04-12T11:05:51Z
dc.date.available	2021-04-12T11:05:51Z
dc.date.issued	2020
dc.identifier.citation	Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2020, 00, pp. 3235-3242
dc.identifier.isbn	9781728150239
dc.identifier.issn	2473-9936
dc.identifier.uri	http://hdl.handle.net/10453/148042
dc.description.abstract	© 2019 IEEE. Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. However, the generated synthetic point cloud data are still missing the artefacts usually exist in point cloud data from real 3D LiDAR sensors. Thus, in this work, we are proposing a domain adaptation framework for bridging this gap between synthetic and real point cloud data. Our proposed framework is based on the deep cycle-consistent generative adversarial networks (CycleGAN) architecture. We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors. The framework has shown competitive results with an improvement of more than 7% in average precision score over other baseline approaches when tested on real BEV point cloud images.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
dc.relation.ispartof	IEEE/CVF International Conference on Computer Vision Workshop
dc.relation.ispartofseries	IEEE International Conference on Computer Vision Workshops
dc.relation.isbasedon	10.1109/ICCVW.2019.00404
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Domain adaptation for vehicle detection from bird's eye view lidar point cloud data
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Seoul, Korea (South)
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-12T11:05:50Z
pubs.finish-date	2019-10-28
pubs.place-of-publication	Psicataway, USA
pubs.publication-status	Published
pubs.start-date	2019-10-27
pubs.volume	00
dc.location	Psicataway, USA

Abstract:

© 2019 IEEE. Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. However, the generated synthetic point cloud data are still missing the artefacts usually exist in point cloud data from real 3D LiDAR sensors. Thus, in this work, we are proposing a domain adaptation framework for bridging this gap between synthetic and real point cloud data. Our proposed framework is based on the deep cycle-consistent generative adversarial networks (CycleGAN) architecture. We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors. The framework has shown competitive results with an improvement of more than 7% in average precision score over other baseline approaches when tested on real BEV point cloud images.

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http://hdl.handle.net/10453/148042