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- W1991634566 abstract "A new model-based human body tracking framework with learned-based theory is proposed in this paper. Thisframework introduces a likely model set-variable structure multiple models (LMS-VSMM) to track articulated humanmotion in monocular images sequences. The key joint points are selected as image feature, which are detectedautomatically and the undetected points are estimated with Particle filters, multiple motion models are learned fromCMU motion capture database with ridge regression method to direct tracking. In tracking, motion models currently ineffect switches from one to another in order to match the present human motion mode. The motion model is activatedaccording to the change in projection angle of kinematic chain, and topological and compatibility relationship amongthem. It is terminated according to their model probabilities. And likely model set schemes of VSMM is used to estimatethe quaternion vectors of joints rotation. Experiments using two videos demonstrate this tracking framework is efficientwith respect to 3D pose and 2D projection." @default.
- W1991634566 created "2016-06-24" @default.
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- W1991634566 date "2011-05-13" @default.
- W1991634566 modified "2023-09-23" @default.
- W1991634566 title "Human body tracking using LMS-VSMM from monocular video sequences" @default.
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- W1991634566 doi "https://doi.org/10.1117/12.883604" @default.
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