Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84313
Title: Human pose tracking by parametric annealing
Authors: Kaliamoorthi, Prabhu.
Kakarala, Ramakrishna.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Kaliamoorthi, P., & Kakarala, R. (2012). Human pose tracking by parametric annealing. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.36-41.
Abstract: Model based methods to marker-free motion capture have a very high computational overhead. In this paper we describe a method that improves on existing global optimization techniques to tracking articulated objects. Our method improves on the state-of-the-art Annealed Particle Filter (APF) by reusing samples across annealing layers and by using an adaptive parametric density for diffusion. We compare the proposed method with APF on a scalable problem and study the effects of dimensionality, multi-modality and the range of search. We perform sensitivity analysis on the parameters of our algorithm and show that it is widely tolerant. We also show results on tracking human pose from the widely-used Human Eva I dataset. Our results show that the proposed method reduces the tracking error despite using less than 50% of the computational resources as APF. The tracked output also shows a significant qualitative improvement over APF.
URI: https://hdl.handle.net/10356/84313
http://hdl.handle.net/10220/16352
DOI: 10.1109/CVPRW.2012.6239235
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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