Journal of Information Technology & Software Engineering
Open Access

Opening the black box: Hierarchical sampling optimization for estimating human hand pose

2^nd Global Summit and Expo Multimedia & Applications

August 15-16, 2016 London, UK

Tae-Kyun Kim

Imperial College London, UK

Posters & Accepted Abstracts: J Inform Tech Softw Eng

Abstract :

We address the problem of hand pose estimation, formulated as an inverse problem. Typical approaches optimize an energy function over pose parameters using a �black box� image generation procedure. This procedure knows little about either the relationships between the parameters or the form of the energy function. In this talk, we show that we can significantly improve upon black box optimization by exploiting high-level knowledge of the parameter structure and using a local surrogate energy function. Our new framework, hierarchical sampling optimization, consists of a sequence of predictors organized into a kinematic hierarchy. Each predictor is conditioned on its ancestors, and generates a set of samples over a subset of the pose parameters. The highly-efficient surrogate energy is used to select among samples. Having evaluated the full hierarchy, the partial pose samples are concatenated to generate a full-pose hypothesis. Several hypotheses are generated using the same procedure, and finally the original full energy function selects the best result. Experimental evaluation on three publically available datasets shows that our method is particularly impressive in low-compute scenarios where it significantly outperforms all other state-of-the-art methods.

Biography :

Email: tk.kim@imperial.ac.uk