Title: Human Body Orientation Estimation in Multiview Scenarios
Abstract: Estimation of human body orientation is an important cue to study and understand human behaviour, for different tasks such as video surveillance or human-robot interaction. In this paper, we propose an approach to simultaneously estimate the body orientation of multiple people in multi-view scenarios, which combines a 3D human body shape and appearance model with a 2D template matching approach. In particular, the 3D model is composed of a generic shape made up of elliptic cylinders, and a 3D colored point cloud (appearance model), obtained by back-projecting pixels from foreground images onto the geometric surfaces. In order to match the reconstructed appearance to target images in arbitrary poses, the appearance is re-projected onto each of the different views, by generating multiple templates that are pixel-wise, robustly matched to the respective foreground images. The effectiveness of the proposed approach is demonstrated through experiments in indoor sequences with manually-labeled ground truth, using a calibrated multi-camera setup.