TY - GEN
T1 - Comparative performance evaluation of GM-PHD filter in clutter
AU - Juang, Radford
AU - Burlina, Philippe
PY - 2009
Y1 - 2009
N2 - Random Finite Sets (RFS) offer a diligent formalism for tracking an unknown number of targets with multiple sensors. The Probability Hypothesis Density (PHD) filter, and its Gaussian Mixture (GM) and Sequential Monte Carlo (SMC) implementations, provide tractable Bayesian Filtering methods that propagate the first order moment of the RFS probability density. A feature of the PHD filters is that they do not require association to complete their correction step. This, we believe, should constitute a significant advantage, especially in scenarios of high false alarm rates and track intersections, which can easily compromise most observerpredictor methods that must perform association to carry out their correction step. To test this hypothesis, we compare the performance of the GM-PHD to the traditional Kalman (KF) and SMC filters for visual tracking of multiple targets in moderate to heavy false alarm rate scenarios. Our tracking and association performance results seem to support this hypothesis.
AB - Random Finite Sets (RFS) offer a diligent formalism for tracking an unknown number of targets with multiple sensors. The Probability Hypothesis Density (PHD) filter, and its Gaussian Mixture (GM) and Sequential Monte Carlo (SMC) implementations, provide tractable Bayesian Filtering methods that propagate the first order moment of the RFS probability density. A feature of the PHD filters is that they do not require association to complete their correction step. This, we believe, should constitute a significant advantage, especially in scenarios of high false alarm rates and track intersections, which can easily compromise most observerpredictor methods that must perform association to carry out their correction step. To test this hypothesis, we compare the performance of the GM-PHD to the traditional Kalman (KF) and SMC filters for visual tracking of multiple targets in moderate to heavy false alarm rate scenarios. Our tracking and association performance results seem to support this hypothesis.
KW - High false alarm rate
KW - PHD filtering
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M3 - Conference contribution
AN - SCOPUS:70449403643
SN - 9780982443804
T3 - 2009 12th International Conference on Information Fusion, FUSION 2009
SP - 1195
EP - 1202
BT - 2009 12th International Conference on Information Fusion, FUSION 2009
T2 - 2009 12th International Conference on Information Fusion, FUSION 2009
Y2 - 6 July 2009 through 9 July 2009
ER -