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dc.contributor.authorChen, Hua-Tsungen_US
dc.contributor.authorChu, Ming-Chuen_US
dc.contributor.authorChou, Chien-Lien_US
dc.contributor.authorLee, Suh-Yinen_US
dc.contributor.authorLin, Bao-Shuhen_US
dc.date.accessioned2017-04-21T06:48:15Z-
dc.date.available2017-04-21T06:48:15Z-
dc.date.issued2015en_US
dc.identifier.isbn978-1-4799-7079-7en_US
dc.identifier.issn2330-7927en_US
dc.identifier.urihttp://hdl.handle.net/11536/136027-
dc.description.abstractTunnel traffic security has received increasing attention since accidents in tunnels may cause serious casualties. Surveillance cameras are widely equipped in tunnels for traffic condition monitoring and safety maintenance. Vehicle identification among multiple cameras is an essential component in tunnel surveillance systems. In this paper, we propose a Spatiotemporal Successive Dynamic Programming ((SDP)-D-2) algorithm for identifying vehicles between pairs of cameras. Taking color information into consideration, we extract features based on Harris corner detection with OpponentSIFT descriptors. "Tracking-by-identification" for vehicles across multiple cameras can thus be achieved. Extensive experiments on real tunnel video data show that the proposed (SDP)-D-2 algorithm outperforms state-of-the-art methods.en_US
dc.language.isoen_USen_US
dc.subjectIntelligent transportation systemen_US
dc.subjectvideo surveillanceen_US
dc.subjecttunnel surveillanceen_US
dc.subjectmulti-camera trackingen_US
dc.subjectvehicle identificationen_US
dc.titleMULTI-CAMERA VEHICLE IDENTIFICATION IN TUNNEL SURVEILLANCE SYSTEMen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000380531100053en_US
dc.citation.woscount0en_US
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