@InProceedings{SergioEscalera2012,
author="Sergio Escalera",
editor="F.J. Perales
and R.B. Fisher
and T.B. Moeslund",
title="Human Behavior Analysis From Depth Maps",
booktitle="7th Conference on Articulated Motion and Deformable Objects",
year="2012",
publisher="Springer Heidelberg",
volume="7378",
pages="282--292",
abstract="Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect {\backslash}textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed.",
optnote="MILAB; HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2040), last updated on Wed, 21 Jun 2017 09:12:18 +0200",
isbn="978-3-642-31566-4",
issn="0302-9743",
doi="10.1007/978-3-642-31567-1_28"
}