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25 August 2004Multiple hypothesis clustering and multiple frame assignment tracking
Tracking and initiating large numbers of closely spaced objects
can pose significant real-time challenges to current
state-of-the-art tracking systems. Cluster or group tracking has
been suggested to reduce the computational complexity when closely
spaced targets move with similar dynamical properties. While
modern individual object tracking systems make association
decisions over multiple frames of data, most cluster tracking
systems make single-frame clustering decisions. In this paper we
illustrate an extension of multiple frame assignment (MFA)
individual object tracking to multiple frame cluster MFA tracking.
In our approach, multiple single-frame clustering hypotheses are
formed and the best clustering is selected over multiple frames of
data. In recent work we formulated multiple frame cluster tracking
assignment problems and demonstrated a single-frame cluster MFA
tracking architecture. The work discussed in this paper extends
the previous work and illustrates a multiple hypothesis clustering,
multiple frame assignment (MHC-MFA), tracking system. We present
simulations studies that motivate the benefits of the multiple
frame cluster tracking approach over single-frame cluster tracking
and discuss the computational efficiency of the multiple frame
cluster tracking approach.
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Sabino Gadaleta, Aubrey B. Poore, Sean Roberts, Benjamin J Slocumb, "Multiple hypothesis clustering and multiple frame assignment tracking," Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); https://doi.org/10.1117/12.542213