Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107951
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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 Responsibility: | 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 |
Grant ID: | http://purl.org/au-research/grants/arc/LP130100154 |
Published version: | http://dx.doi.org/10.1109/iccv.2015.349 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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RA_hdl_107951.pdf Restricted Access | Restricted Access | 1.25 MB | Adobe PDF | View/Open |
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