# Does this graphical model describe a hidden Markov model?

I'm facing the problem of visual tracking in computer vision. I have some observation (image blobs by background subtraction) produced by some moving object, and the task is to infer the state (position, velocity) of the object given the observation. I assume that each state depends only on the immediate predecessor (Markov assumption), ruled by a temporal model (the dynamics) that describes how a state $X_t$ evolve in $X_{t+1}$, and that the measurement model do not depends on time, so that $y_t$ do not depends on $X_{t-1}$ but only on $X_t$.

Everywhere in the web I have found that this problem can be described by this graphical model:

For inferring the posterior distribution, after receiving new measurements, a Kalman filter or Particle filter is used.

But anywhere I explicitly read that this is an Hidden Markov Model.

Is this a HMM? Why? Why not?