File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A Novel Object Segmentation Method for Silhouette Tracker in Video Surveillance Application

TitleA Novel Object Segmentation Method for Silhouette Tracker in Video Surveillance Application
Authors
Issue Date2014
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6820887
Citation
International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, Nevada, USA, 9-12 March 2014. In International Conference on Computational Science and Computational Intelligence Proceedings, 2014, v. 1, p. 103-107, article no. 6822091 How to Cite?
AbstractIn recent years, surveillance cameras are deployed almost everywhere. More and more video analytics features have been developed and incorporated with video surveillance system for conducting intelligence tasks, such as motion detection, human identification, etc. One typical requirement is to track suspicious humans or vehicles in the cameras' live or recorded footages, and over the years researchers have proposed different tracking methods, such as point tracking, kernel tracking and silhouette tracking to support this requirement. In particular, silhouette tracker has received considerable attention because it works well for objects with a large variety of shape, provided that reasonably good object masks or contours are initialized properly for the silhouette tracker. A properly initialized object mask and contour, however, cannot be obtained easily. On one hand, a simple bounding box contains too much irrelevant background objects, while a manually specified mask could provide accurate silhouette but this also requires lots of interactive which greatly limits its practicality. In this paper, we present a novel block based object mask segmentation method for silhouette tracker initialization. Essentially, the proposed method re-uses the motion information extracted during the video encoding phase, which provides approximated object masks for silhouette tracker. Experimental results confirm that such a block-based object masks is sufficient for a robust silhouette tracker to reliably track moving objects. © 2014 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/203653
ISBN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLuo, Ten_US
dc.contributor.authorChung, HYen_US
dc.contributor.authorChow, KPen_US
dc.date.accessioned2014-09-19T15:49:10Z-
dc.date.available2014-09-19T15:49:10Z-
dc.date.issued2014en_US
dc.identifier.citationInternational Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, Nevada, USA, 9-12 March 2014. In International Conference on Computational Science and Computational Intelligence Proceedings, 2014, v. 1, p. 103-107, article no. 6822091en_US
dc.identifier.isbn9781479930104-
dc.identifier.urihttp://hdl.handle.net/10722/203653-
dc.description.abstractIn recent years, surveillance cameras are deployed almost everywhere. More and more video analytics features have been developed and incorporated with video surveillance system for conducting intelligence tasks, such as motion detection, human identification, etc. One typical requirement is to track suspicious humans or vehicles in the cameras' live or recorded footages, and over the years researchers have proposed different tracking methods, such as point tracking, kernel tracking and silhouette tracking to support this requirement. In particular, silhouette tracker has received considerable attention because it works well for objects with a large variety of shape, provided that reasonably good object masks or contours are initialized properly for the silhouette tracker. A properly initialized object mask and contour, however, cannot be obtained easily. On one hand, a simple bounding box contains too much irrelevant background objects, while a manually specified mask could provide accurate silhouette but this also requires lots of interactive which greatly limits its practicality. In this paper, we present a novel block based object mask segmentation method for silhouette tracker initialization. Essentially, the proposed method re-uses the motion information extracted during the video encoding phase, which provides approximated object masks for silhouette tracker. Experimental results confirm that such a block-based object masks is sufficient for a robust silhouette tracker to reliably track moving objects. © 2014 IEEE.-
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6820887en_US
dc.relation.ispartofInternational Conference on Computational Science and Computational Intelligence Proceedingsen_US
dc.rightsInternational Conference on Computational Science and Computational Intelligence Proceedings. Copyright © I E E Een_US
dc.rights©2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA Novel Object Segmentation Method for Silhouette Tracker in Video Surveillance Applicationen_US
dc.typeConference_Paperen_US
dc.identifier.emailChung, HY: hychung@cs.hku.hken_US
dc.identifier.emailChow, KP: chow@cs.hku.hken_US
dc.identifier.authorityChung, HY=rp00219en_US
dc.identifier.authorityChow, KP=rp00111en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CSCI.2014.24en_US
dc.identifier.scopuseid_2-s2.0-84902656946-
dc.identifier.hkuros240076en_US
dc.identifier.volume1en_US
dc.identifier.spage103en_US
dc.identifier.epage107, article no. 6822091en_US
dc.identifier.isiWOS:000355911900017-
dc.publisher.placeUnited Statesen_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats