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in the previous video we described the PMB M conjugate prior for extended object tracking in this video we give a high-level description of how the PMB M conjugate prior can be used to obtain a recursive algorithm since many of the details are emitted it also serves as a generic description of several other extended object tracking algorithms the way we previously described the global hypotheses in a p.m. B M filter for extended objects is useful for understanding how many hypotheses we have however in our recursive filters we normally have the local tracks here represented by a potential object and its local hypotheses that we propagate over time as an example suppose we have one measurement at time 1 0 measurements at time 2 and 2 measurements at time 3 in that case there are four potential objects in total even though they cannot all coexist and the total set of local hypotheses is illustrated in the figure compared to the PM BM s for point objects we now have two new local hypothes
