@article {10.3844/jcssp.2017.795.804,
article_type = {journal},
title = {Multi-View RGB-D Video Analysis and Fusion for 360 Degrees Unified Motion Reconstruction},
author = {Ahmed, Naveed},
volume = {13},
number = {12},
year = {2017},
month = {Dec},
pages = {795-804},
doi = {10.3844/jcssp.2017.795.804},
url = {https://thescipub.com/abstract/jcssp.2017.795.804},
abstract = {We present a new method for capturing human motion over 360 degrees by the fusion of multi-view RGB-D video data from Kinect sensors. Our method is able to reconstruct the unified human motion from fused RGB-D and skeletal data over 360 degrees and create a unified skeletal animation. We make use of all three streams: RGB, depth and skeleton, along with the joint tracking confidence state from Microsoft Kinect SDK to find the correctly oriented skeletons and merge them together to get a uniform measurement of human motion resulting in a unified skeletal animation. We quantitatively validate the goodness of the unified motion using two evaluation techniques. Our method is easy to implement and provides a simple solution of measuring and reconstructing a 360 degree plausible unified human motion that would not be possible to capture with a single Kinect due to tracking failures, self-occlusions, limited field of view and subject orientation.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}