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

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I have a problem. I have read many papers about video stabilization, almost using paper fiction filter to smoothen the speed As mentioned, it is strong and is run in real-time applications. But another filter is also strongly, which is the particle filter. But why do not we use Patalus Filters in the chutney to create stabilized videos? Some papers use particle filters to estimate the global speed between frames (speed estimate part). They are difficult to understand. Can someone explain me to me, please? Thanks a lot.

An imaginary filter is uni-modal This means that a normal There is a belief with the co-friendship matrix, an error to represent self confidence in this belief as distribution. If you are going to smooth some process, you want to get a singular, smooth result. It is analogous to a KF. It is like using the regression of at least sections to fit a line for the data, you are simplifying the input into a result.

A particle filter is by its nature multidimensional where a Cayman filter shows belief as a central value and makes a variance around that central value, a particle filter There are many particles in whose values ​​are classified around those areas which are more likely. A particle filter can be may basically represent a similar state (such as the histogram of particles which resembles the classic bell curve of normal distribution). But there are many humps in a particle filter or in fact there is any shape. The ability of many simultaneous modes is appropriate for handling problems like estimating speed, because a mode (cluster of particles) can represent one step, and the second mode represents a different move, this ambiguity Upon submission, a KF will have to leave a possibility completely, but a particle filter can believe both things at the same time, as long as the data is more It does not solve the ambiguity.

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