Gidday cobbers/esteemed colleagues,

With multi-object tracking that implements Kalman prediction/correction the general approach I see suggested in other SO threads is to simply have a vector/array of Kalman filters for each object.

i.e. 'multiple-single-object Kalman filters'

But knowing that if you define your state space matrices correctly, states that are independent of each other will remain so once any (coherent) math is said and done - why don't we just augment the various state and associated matrices/vectors involved in a filter with *all* the object 'data' and use *one* Kalman filter? (yes, there will be lots of zeros in most of the matrices).

Is there any algorithmic complexity advantage either way? My intuition is that using one filter vs. many might reduce overhead?

Maybe is it just easier to manage in terms of human readability in dealing with multiple filters?

Any other reasons?

Thanks

p.s. eventual code will be in openCV/C++