Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/99308
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dc.contributor.authorWei-Cheng Wangzh_TW
dc.contributor.authorChien-Yu Chiouzh_TW
dc.contributor.authorChun-Rong Huangzh_TW
dc.contributor.authorPau-Choo Chungzh_TW
dc.contributor.authorWei-Yun Huangzh_TW
dc.contributor.author黃春融zh_TW
dc.date2018-03-03-
dc.date.accessioned2019-12-19T06:34:51Z-
dc.date.available2019-12-19T06:34:51Z-
dc.identifier.urihttp://hdl.handle.net/11455/99308-
dc.description.abstractIn this paper, we propose a novel annotation placement approach for revealing information about foreground objects in surveillance videos. To arrange positions of annotations, spatiotemporal coherence between annotations and foreground objects is applied. The annotation placement problem is formulated as an optimization problem with respect to spatiotemporal coherence of annotations and foreground objects. The optimization problem is effectively solved using Markov random fields. To the best of our knowledge, this paper is the first work that discusses and solves the annotation placement problem for surveillance videos by considering the relationships between annotations and foreground objects with trajectories. As shown in the experiments, the proposed approach can arrange annotations based on the moving trajectories of foreground objects and prevent the occlusions between different annotations and foreground objects. It also achieves better quantitative and qualitative results compared with state-of-the-art approaches.zh_TW
dc.language.isoenzh_TW
dc.publisherIEEE Transactions on Circuits and Systems for Video Technologyzh_TW
dc.relationIEEE Transactions on Circuits and Systems for Video Technology ( Volume: 28 , Issue: 3 , March 2018 )zh_TW
dc.relation.urihttps://ieeexplore.ieee.org/document/7744579zh_TW
dc.subjectFeature representationzh_TW
dc.subjectMarkov random fields (MRFs)zh_TW
dc.subjectvideo surveillancezh_TW
dc.titleSpatiotemporal Coherence-Based Annotation Placement for Surveillance Videoszh_TW
dc.typeJournal Articlezh_TW
dc.identifier.doi10.1109/TCSVT.2016.2629340zh_TW
dc.awards2018zh_TW
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