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ICCV
2007
IEEE

Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering

8 years 12 months ago
Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering
Even though sensor fusion techniques based on particle filters have been applied to object tracking, their implementations have been limited to combining measurements from multiple sensors by the simple product of individual likelihoods. Therefore, the number of observations is increased as many times as the number of sensors, and the combined observation may become unreliable through blind integration of sensor observations — especially if some sensors are too noisy and non-discriminative. We describe a methodology to model interactions between multiple sensors and to estimate the current state by using a mixture of Bayesian filters — one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, an adaptive particle arrangement system is constructed in which each particle is allocated to only one of the sensors for observation and a different number of samples is assigned to ea...
Bohyung Han, Seong-Wook Joo, Larry S. Davis
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2007
Where ICCV
Authors Bohyung Han, Seong-Wook Joo, Larry S. Davis
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