Zero-VIRUS*: Zero-shot Vehicle Route Understanding System for Intelligent Transportation

Yu, L; Feng, Q; Qian, Y; Liu, W; Hauptmann, AG

Zero-VIRUS*: Zero-shot Vehicle Route Understanding System for Intelligent Transportation

Yu, L Feng, Q

Qian, Y Liu, W Hauptmann, AG

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, 2020-June, pp. 2534-2543
Issue Date:: 2020-07-28

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Field	Value	Language
dc.contributor.author	Yu, L
dc.contributor.author	Feng, Q https://orcid.org/0000-0003-1014-6081
dc.contributor.author	Qian, Y
dc.contributor.author	Liu, W
dc.contributor.author	Hauptmann, AG
dc.date	2020-06-14
dc.date.accessioned	2021-04-15T10:19:04Z
dc.date.available	2021-04-15T10:19:04Z
dc.date.issued	2020-07-28
dc.identifier.citation	2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, 2020-June, pp. 2534-2543
dc.identifier.isbn	978-1-7281-9361-8
dc.identifier.issn	2160-7508
dc.identifier.issn	2160-7516
dc.identifier.uri	http://hdl.handle.net/10453/148142
dc.description.abstract	Nowadays, understanding the traffic statistics in real city-scale camera networks takes an important place in the intelligent transportation field. Recently, vehicle route understanding brings a new challenge to the area. It aims to measure the traffic density by identifying the route of each vehicle in traffic cameras. This year, the AI City Challenge holds a competition with real-world traffic data on vehicle route understanding, which requires both efficiency and effectiveness. In this work, we propose Zero-VIRUS, a Zeroshot VehIcle Route Understanding System, which requires no annotation for vehicle tracklets and is applicable for the changeable real-world traffic scenarios. It adopts a novel 2D field modeling of pre-defined routes to estimate the proximity and completeness of each track. The proposed system has achieved third place on Dataset A in stage 1 of the competition (Track 1: Vehicle Counts by Class at Multiple Intersections) against world-wide participants on both effectiveness and efficiency, with a record of the top place on 50% of the test set.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
dc.relation.ispartof	2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
dc.relation.isbasedon	10.1109/cvprw50498.2020.00305
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Zero-VIRUS*: Zero-shot Vehicle Route Understanding System for Intelligent Transportation
dc.type	Conference Proceeding
utslib.citation.volume	2020-June
utslib.location.activity	Seattle, WA, USA
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/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-15T10:19:01Z
pubs.finish-date	2020-06-19
pubs.publication-status	Published
pubs.start-date	2020-06-14
pubs.volume	2020-June

Abstract:

Nowadays, understanding the traffic statistics in real city-scale camera networks takes an important place in the intelligent transportation field. Recently, vehicle route understanding brings a new challenge to the area. It aims to measure the traffic density by identifying the route of each vehicle in traffic cameras. This year, the AI City Challenge holds a competition with real-world traffic data on vehicle route understanding, which requires both efficiency and effectiveness. In this work, we propose Zero-VIRUS, a Zeroshot VehIcle Route Understanding System, which requires no annotation for vehicle tracklets and is applicable for the changeable real-world traffic scenarios. It adopts a novel 2D field modeling of pre-defined routes to estimate the proximity and completeness of each track. The proposed system has achieved third place on Dataset A in stage 1 of the competition (Track 1: Vehicle Counts by Class at Multiple Intersections) against world-wide participants on both effectiveness and efficiency, with a record of the top place on 50% of the test set.

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http://hdl.handle.net/10453/148142