Presentation on theme: "Motivating Markov Chain Monte Carlo for Multiple Target Tracking"— Presentation transcript:
1 Motivating Markov Chain Monte Carlo for Multiple Target Tracking Krishna
2 Overview Single Target Tracking : Bayes filter. Multiple Target Tracking : Extending Bayes filter to Joint Probabilistic Data Association Filter (JPDAF).JPDAF is NP Hard. Extend JPDAF to MCMC.
3 Basic Concepts Law of Total Probability Markov Process Locating an ObjectBayes RuleObservationPriorPosterior
4 Single -Target Tracking : Problem Definition Consider tracking 1 Object.state of a single object at time kNoisy observation- time kis the sequence of all measurements upto time kHow to estimate the state for observations ?
5 Bayes Filters Motion Model Observation Model Predict : Update : P(Current State | previous observations)P(Current State | Previous State)Motion Model !P(Previous State | Previous Observations)Update :P(Current State | Current & previous observations)P(Current Observation | Current State)Observation Model !P(Current State | previous observations)
6 Kalman Filter : Specialization of Baye’s Filter Assumptions of Kalman Filter:Predicted StateObservation
7 Multi-Target Tracking : Problem Definition Consider tracking T Objects.State of these objects at time k :is the state space of a single object.is observation at time kis one such observation.is the sequence of all observations upto time kHow to assign the observed observations to individual objects ?Simultaneously Assign and Track
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