title: Kalman filter in computer vision: the choice of Q and R noise covariances
body: <p>I read some works about Kalman filter for CV object tracking but I can't find some reference about the choice of: 1)the process noise covariance Q; 2)Measurement noise covariance R. So far I have realized that the model is equation of motion (someone uses acceleration as state variable, others use position and speed only) but nobody is clear about Q and R choice including this example by mathworks: <a href="http://www.mathworks.it/it/help/vision/examples/using-kalman-filter-for-object-tracking.html" rel="noreferrer">http://www.mathworks.it/it/help/vision/examples/using-kalman-filter-for-object-tracking.html</a>
  Recently I found this page: 
  <a href="http://blog.cordiner.net/2011/05/03/object-tracking-using-a-kalman-filter-matlab/" rel="noreferrer">http://blog.cordiner.net/2011/05/03/object-tracking-using-a-kalman-filter-matlab/</a>
  but the Q and R assignment is not clear. Does anyone know help me, please ?</p>