Smoothing motion by using Kalman Filter or Particle Filter in video stabilization

Question

I have a problem. I have read many papers about video stabilization. Almost papers mention about smoothing motion by using Kalman Filter, so it's strong and run in real-time applications. But there is also another filter strongly, that is particle filter. But why dont we use Partilce filter in smoothing motion to create stabilized video? Some papers only use particle filter in estimating global motion between frames (motion estimation part). It is hard to understand them. Can anyone explain them for me, please? Thank you so much.

Answer 1

A Kalman Filter is uni-modal. That means it has one belief along with an error covariance matrix to represent the confidence in that belief as a normal distribution. If you are going to smooth some process, you want to get out a single, smoothed result. This is consistent with a KF. It's like using least squares regression to fit a line to data. You are simplifying the input to one result.

A particle filter is multi-modal by its very nature. Where a Kalman Filter represents belief as a central value and a variance around that central value, a particle filter just has many particles whose values are clustered around regions that are more likely. A particle filter can represent essentially the same state as a KF (imagine a histogram of the particles that looks like the classic bell curve of the normal distribution). But a particle filter can also have multiple humps or really any shape at all. This ability to have multiple simultaneous modes is ideally suited to handle problems like estimating motion, because one mode (cluster of particles) can represent one move, and another mode represents a different move. When presented with this ambiguity, a KF would have to abandon one of the possibilities altogether, but a particle filter can keep on believing both things at the same time until the ambiguity is resolved by more data.