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dc.contributor.authorChen, Zhexu (Michael)
dc.contributor.authorChen, Lei
dc.contributor.authorCetin, Mujdat
dc.contributor.authorWillsky, Alan S.
dc.date.accessioned2012-10-04T13:16:10Z
dc.date.available2012-10-04T13:16:10Z
dc.date.issued2009-08
dc.date.submitted2009-07
dc.identifier.isbn978-0-9824-4380-4
dc.identifier.urihttp://hdl.handle.net/1721.1/73586
dc.description.abstractWe propose a new approach for multi-sensor multi-target tracking by constructing statistical models on graphs with continuous-valued nodes for target states and discrete-valued nodes for data association hypotheses. These graphical representations lead to message-passing algorithms for the fusion of data across time, sensor, and target that are radically different than algorithms such as those found in state-of-the-art multiple hypothesis tracking (MHT) algorithms. Important differences include: (a) our message-passing algorithms explicitly compute different probabilities and estimates than MHT algorithms; (b) our algorithms propagate information from future data about past hypotheses via messages backward in time (rather than doing this via extending track hypothesis trees forward in time); and (c) the combinatorial complexity of the problem is manifested in a different way, one in which particle-like, approximated, messages are propagated forward and backward in time (rather than hypotheses being enumerated and truncated over time). A side benefit of this structure is that it automatically provides smoothed target trajectories using future data. A major advantage is the potential for low-order polynomial (and linear in some cases) dependency on the length of the tracking interval N, in contrast with the exponential complexity in N for so-called N-scan algorithms. We provide experimental results that support this potential. As a result, we can afford to use longer tracking intervals, allowing us to incorporate out-of-sequence data seamlessly and to conduct track-stitching when future data provide evidence that disambiguates tracks well into the past.en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5203742en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceIEEEen_US
dc.titleAn Efficient Message Passing Algorithm for Multi-Target Trackingen_US
dc.typeArticleen_US
dc.identifier.citationZhexu Chen, et al. "An efficient message passing algorithm for multi-target tracking" 12th International Conference on Information Fusion, 2009. FUSION '09. ©2009 ISIFen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.contributor.mitauthorChen, Zhexu (Michael)
dc.contributor.mitauthorChen, Lei
dc.contributor.mitauthorCetin, Mujdat
dc.contributor.mitauthorWillsky, Alan S.
dc.relation.journalProceedings of the 12th International Conference on Information Fusion, 2009. FUSION '09en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-0149-5888
mit.licensePUBLISHER_POLICYen_US


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