Collaborative voting of 3D features for robust gesture estimation

Daniel van Sabben; Javier Ruiz Hidalgo; Xavier Suau Cuadros; Josep R. Casas

ICASSP 2017accepted0 citations

Collaborative voting of 3D features for robust gesture estimation

Daniel van Sabben, Javier Ruiz Hidalgo, Xavier Suau Cuadros, Josep R. Casas

Abstract

Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each selected point (anchor point) contributes to the estimation by casting votes. The main idea is to detect a body part by accumulating the inference of other trained body parts. The collaborative voting encodes the global context of human pose, while 3D features represent local appearance. Body parts contributing to the detection are interpreted as a voting process. Experimental results for different 3D features prove the validity of the proposed algorithm.

BibTeX

@inproceedings{icassp2017_collaborativevot,
  title = {Collaborative voting of 3D features for robust gesture estimation},
  author = {Daniel van Sabben and Javier Ruiz Hidalgo and Xavier Suau Cuadros and Josep R. Casas},
  booktitle = {ICASSP 2017},
  year = {2017}
}