• جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 1048
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     we present a novel off-line algorithm for target segmentation and tracking in video. in our approach, video data is represented by a multi-label markov random field model, and segmentation is accomplished by finding the minimum energy label assignment. we propose a novel energy formulation which incorporates both segmentation and motion estimation in a single framework. our energy functions enforce motion coherence both within and across frames. we utilize state-of-the-art methods to efficiently optimize over a large number of discrete labels. in addition, we introduce a new ground-truth dataset, called georgia tech segmentation and tracking dataset (gt-segtrack), for the evaluation of segmentation accuracy in video tracking. we compare our method with several recent on-line tracking algorithms and provide quantitative and qualitative performance comparisons.

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