NDLI: Multiple view human articulated tracking using charting and particle swarm optimisation

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Multiple view human articulated tracking using charting and particle swarm optimisation

Content Provider	ACM Digital Library
Author	Trucco, Emanuele John, Vijay
Abstract	We present a framework for markerless articulated human motion tracking in multi-view sequences. We learn motion models of common actions in a low-dimensional latent space using charting, a nonlinear dimensionality reduction took which estimates automatically the dimension of the latent space and keeps similar poses close together in it. Additionally charting obtains the inverse mapping from the low dimensional latent space to the high-dimensional joint angle space. The tracking is formulated as a low-dimensional nonlinear optimisation in the latent space and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve di±cult nonlinear optimisation problems. Tracking results with the walking, kicking, praying, posing and punch sequences demonstrate the good accuracy and performance of our approach.
Starting Page	51
Ending Page	56
Page Count	6
File Format	PDF
ISBN	9781450301596
DOI	10.1145/1877791.1877805
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dimensionality reduction Particle swarm optimisation Articulated tracking Charting
Content Type	Text
Resource Type	Article

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