Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/106854
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | Real-time tracking of single and multiple objects from depth-colour imagery using 3D signed distance functions |
Author: | Ren, C. Prisacariu, V. Kähler, O. Reid, I. Murray, D. |
Citation: | International Journal of Computer Vision, 2017; 124(1):80-95 |
Publisher: | Springer |
Issue Date: | 2017 |
ISSN: | 0920-5691 1573-1405 |
Statement of Responsibility: | C. Y. Ren, V. A. Prisacariu, O. Kähler, I. D. Reid, D. W. Murray |
Abstract: | We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depth-colour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on these functions are developed to account for the observed RGB-D imagery, and tracking is posed as a maximum a posteriori problem. We present first a method suited to tracking a single rigid 3D object, and then generalise this to multiple objects by combining distance functions into a shape union in the frame of the camera. This second model accounts for similarity and proximity between objects, and leads to robust real-time tracking without recourse to bolt-on or ad-hoc collision detection. |
Keywords: | Multi-object tracking; depth tracking; RGB-D imagery; signed distance functions; real-time |
Rights: | © The Author(s) 2017. This article is published with open access at Springerlink.com |
DOI: | 10.1007/s11263-016-0978-2 |
Grant ID: | http://purl.org/au-research/grants/arc/FL130100102 |
Published version: | http://dx.doi.org/10.1007/s11263-016-0978-2 |
Appears in Collections: | Aurora harvest 3 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.