Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/106854
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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

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