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- W2153934580 abstract "AbstractWe propose a novel method to detect human poses in videos by concurrently optimizing body part matching and object segmentation. With a single exemplar image, the proposed method detects the poses of a specific human subject in long video sequences. Matching and segmentation support each other and therefore the simultaneous optimization enables more reliable results. However, efficient concurrent optimization is a great challenge due to its huge search space. We propose an efficient linear method that solves the problem. In this method, the optimal body part matching conforms to local appearances and a human body plan, and the body part configuration is consistent with the object foreground estimated by simultaneous superpixel labeling. Our experiments on a variety of videos show that the proposed method is efficient and more reliable than previous locally constrained approaches.KeywordsBody PartTarget ImageObject ForegroundPairwise ConstraintForeground SegmentationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W2153934580 created "2016-06-24" @default.
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- W2153934580 date "2011-01-01" @default.
- W2153934580 modified "2023-10-17" @default.
- W2153934580 title "Finding Human Poses in Videos Using Concurrent Matching and Segmentation" @default.
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- W2153934580 doi "https://doi.org/10.1007/978-3-642-19315-6_18" @default.
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