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dc.contributor.authorTang, Zhengen_US
dc.contributor.authorHwang, Jenq-Nengen_US
dc.contributor.authorLin, Yen-Shuoen_US
dc.contributor.authorChuang, Jen-Huien_US
dc.date.accessioned2017-04-21T06:49:01Z-
dc.date.available2017-04-21T06:49:01Z-
dc.date.issued2016en_US
dc.identifier.isbn978-1-4799-9988-0en_US
dc.identifier.issn1520-6149en_US
dc.identifier.urihttp://hdl.handle.net/11536/136360-
dc.description.abstractIn a video surveillance system with static cameras, object segmentation often fails when part of the object has similar color with the background, resulting in poor performance of the subsequent object tracking. Multiple kernels have been utilized in object tracking to deal with occlusion, but the performance still highly depends on segmentation. This paper presents an innovative system, named Multiple-kernel Adaptive Segmentation and Tracking (MAST), which dynamically controls the decision thresholds of background subtraction and shadow removal around the adaptive kernel regions based on the preliminary tracking results. Then the objects are tracked for the second time according to the adaptively segmented foreground. Evaluations of both segmentation and tracking on benchmark datasets and our own recorded video sequences demonstrate that the proposed method can successfully track objects in similar-color background and/or shadow areas with favorable segmentation performance.en_US
dc.language.isoen_USen_US
dc.subjectAdaptive Segmentationen_US
dc.subjectObject Trackingen_US
dc.subjectMultiple Kernelsen_US
dc.subjectBackground Subtractionen_US
dc.subjectShadow Removalen_US
dc.titleMULTIPLE-KERNEL ADAPTIVE SEGMENTATION AND TRACKING (MAST) FOR ROBUST OBJECT TRACKINGen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGSen_US
dc.citation.spage1115en_US
dc.citation.epage1119en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000388373401051en_US
dc.citation.woscount0en_US
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