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)


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
Number of pages9
ISBN (Electronic)9781467383912, 9781467383905
Publication statusPublished - 2015
Externally publishedYes
EventIEEE International Conference on Computer Vision 2015 - Santiago, Chile
Duration: 7 Dec 201513 Dec 2015
Conference number: 15th (Proceedings)


ConferenceIEEE International Conference on Computer Vision 2015
Abbreviated titleICCV 2015
Internet address

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