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Hi Theodore! Wow that was a long question... but as Jack the Ripper said: "Lets go by parts"

  1. Did you check the sample code for Kalman filter that comes with OpenCV-2.4.3? It is located at OpenCV-2.4.3/samples/cpp/kalman.cpp. Also, I recommend you the Unit 2 of Sebastian Thrun's excellent course about Self-driving cars (It is not related to OpenCV but has an outstanding explanation of Kalman filters)

  2. Yes, you can apply the prediction step as many times you want without measuring. But, you should keep in mind that the uncertainty about the position of your tracked object will grow and grow until you provide a measurement.

  3. Including more parameters, just for the sake of it, will not help you without a good prediction model.

  4. It will depend on the covariance matrix, if the parameters are independent then it will make no difference using only one or several kalman filters. But, if the parameters are somehow interrelated then using different kalman filters for each parameter will produce different results as if you were using only one filter for all parameters. I would recommend you to use the same filter for all the parameters.

  5. The answer to this is on the course that I pointed you out before :) Short version: Velocity can be derived from previous positions.

I hope this could shed some light to your doubts. Welcome to the forum.