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Stuart Russell
UC Berkeley


"Tracking Many Objects With Many Sensors"

5/10/2000: [time not recorded]
[location not recorded]

Abstract: Keeping track of multiple objects over time is a problem that arises in many real-world domains. The problem is often complicated by noisy sensors and unpredictable dynamics. The data association literature describes many algorithms that solve special cases or provide heuristic approaches for various applications. This talk begins by setting up the general problem as an instance of probabilistic inference, and points out its intractability. An earlier method due to Huang and Russell is described and shown not to scale to multiple sensors. It is shown that estimation of intrinsic object properties allows scaling through a decomposition of the global likelihood. The inferenceproblem is solved by a polynomial-time approximation scheme based on Markov chain Monte Carlo simulation. Experiments with a freeway traffic simulation suggest that the method allows accurate estimation of long-range origin/destination information even when the individual links in the sensor chain are highly unreliable. If time permits, I will describe an extension of these ideas to the general problem of inference in first-order probabilistic logics. Joint work with Hanna Pasula, Mike Ostland, Ya'acov Ritov


Last updated: Mon Jun 19 17:44:06 2006

 

 

 

 

 
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