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Type: Conference paper
Title: Joint Probabilistic Data Association Revisited
Author: Rezatofighi, S.
Milan, A.
Zhang, Z.
Shi, Q.
Dick, A.
Reid, I.
Citation: Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, 2015, vol.2015 International Conference on Computer Vision, ICCV 2015, pp.3047-3055
Publisher: IEEE
Issue Date: 2015
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781467383912
ISSN: 1550-5499
Conference Name: 2015 IEEE International Conference on Computer Vision (ICCV 2015) (7 Dec 2015 - 13 Dec 2015 : Santiago, CHILE)
Statement of
Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid
Abstract: In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.
Keywords: Target tracking, probabilistic logic, clutter, surveillance, kalman filters, noise measurement, time measurement
Rights: © 2015 IEEE
DOI: 10.1109/ICCV.2015.349
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