Human motion capture using scalable body models

作者:

Highlights:

摘要

This paper presents a general analysis framework towards exploiting the underlying hierarchical and scalable structure of an articulated object for pose estimation and tracking. Scalable human body models are introduced as an ordered set of articulated models fulfilling an inclusive hierarchy. The concept of annealing is applied to derive a generic particle filtering scheme able to perform a sequential filtering over the set of models contained in the scalable human body model. Two annealing loops are employed, the standard likelihood annealing and the newly introduced structural annealing, leading to a robust, progressive and efficient analysis of the input data. The validity of this scheme is tested by performing markerless human motion capture in a multi-camera environment employing the standard HumanEva annotated datasets. Finally, quantitative results are presented and compared with other existing HMC techniques.

论文关键词:

论文评审过程:Received 25 January 2010, Accepted 6 June 2011, Available online 17 June 2011.

论文官网地址:https://doi.org/10.1016/j.cviu.2011.06.001