Abstract | ||
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We 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. |
Year | Venue | Keywords |
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2009 | Fusion | n-scan algorithm,smoothing,data association hypotheses,data association,message passing algorithm,low-order polynomial dependency,graphical models,tracking interval,target tracking,multisensor multitarget tracking,data fusion,computational complexity,out-of-sequence data,multi-hypothesis tracking,exponential complexity,smoothed target trajectory,message passing,multiple hypothesis tracking algorithm,track-stitching,multi-target tracking,combinatorial complexity,sensor fusion,statistical model |
DocType | ISBN | Citations |
Conference | 978-0-9824-4380-4 | 7 |
PageRank | References | Authors |
0.61 | 4 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zhexu Chen | 1 | 7 | 0.61 |
Lei Chen | 2 | 7 | 0.61 |
Müjdat Çetin | 3 | 1342 | 112.26 |
Alan S. Willsky | 4 | 7466 | 847.01 |