Joint probabilistic data association revisited

Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

178 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015
EditorsKatsushi Ikeuchi, Christoph Schnörr, Josef Sivic, René Vidal
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3047-3055
Number of pages9
ISBN (Electronic)9781467383912, 9781467383905
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventIEEE International Conference on Computer Vision 2015 - Santiago, Chile
Duration: 7 Dec 201513 Dec 2015
Conference number: 15th
https://ieeexplore.ieee.org/xpl/conhome/7407725/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Computer Vision 2015
Abbreviated titleICCV 2015
CountryChile
CitySantiago
Period7/12/1513/12/15
Internet address

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