In this section
@inproceedings{Nunes:2022:10.1109/ICRA46639.2022.9812430,
author = {Nunes, UM and Demiris, Y},
doi = {10.1109/ICRA46639.2022.9812430},
pages = {508--514},
title = {Kinematic Structure Estimation of Arbitrary Articulated Rigid Objects for Event Cameras},
url = {http://dx.doi.org/10.1109/ICRA46639.2022.9812430},
year = {2022}
}
TY - CPAPER
AB - We propose a novel method that estimates the Kinematic Structure (KS) of arbitrary articulated rigid objects from event-based data. Event cameras are emerging sensors that asynchronously report brightness changes with a time resolution of microseconds, making them suitable candidates for motion-related perception. By assuming that an articulated rigid object is composed of body parts whose shape can be approximately described by a Gaussian distribution, we jointly segment the different parts by combining an adapted Bayesian inference approach and incremental event-based motion estimation. The respective KS is then generated based on the segmented parts and their respective biharmonic distance, which is estimated by building an affinity matrix of points sampled from the estimated Gaussian distributions. The method outperforms frame-based methods in sequences obtained by simulating events from video sequences and achieves a solid performance on new high-speed motions sequences, which frame-based KS estimation methods can not handle.
AU - Nunes,UM
AU - Demiris,Y
DO - 10.1109/ICRA46639.2022.9812430
EP - 514
PY - 2022///
SN - 1050-4729
SP - 508
TI - Kinematic Structure Estimation of Arbitrary Articulated Rigid Objects for Event Cameras
UR - http://dx.doi.org/10.1109/ICRA46639.2022.9812430
ER -
