Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/132263
Citations
Scopus Web of Science® Altmetric
?
?
Type: Conference paper
Title: Globally optimal contrast maximisation for event-based motion estimation
Author: Liu, D.
Parra Bustos, Á.
Chin, T.J.
Citation: Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp.6348-6357
Publisher: IEEE
Publisher Place: online
Issue Date: 2020
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781728171685
ISSN: 1063-6919
2575-7075
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (13 Jun 2020 - 19 Jun 2020 : virtual online)
Statement of
Responsibility: 
Daqi Liu, Álvaro Parra, Tat-Jun Chin
Abstract: Contrast maximisation estimates the motion captured in an event stream by maximising the sharpness of the motion-compensated event image. To carry out contrast maximisation, many previous works employ iterative optimisation algorithms, such as conjugate gradient, which require good initialisation to avoid converging to bad local minima. To alleviate this weakness, we propose a new globally optimal event-based motion estimation algorithm. Based on branch-and-bound (BnB), our method solves rotational (3DoF) motion estimation on event streams, which supports practical applications such as video stabilisation and attitude estimation. Underpinning our method are novel bounding functions for contrast maximisation, whose theoretical validity is rigorously established. We show concrete examples from public datasets where globally optimal solutions are vital to the success of contrast maximisation. Despite its exact nature, our algorithm is currently able to process a 50,000-event input in ≈ 300 seconds (a locally optimal solver takes ≈ 30 seconds on the same input). The potentialfor GPU acceleration will also be discussed.
Rights: ©2020 IEEE
DOI: 10.1109/CVPR42600.2020.00638
Grant ID: http://purl.org/au-research/grants/arc/DP200101675
Published version: https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding
Appears in Collections:Aurora harvest 4
Computer Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.