My system is the following. I use the camera of a mobile device to track an object. From this tracking, I get four 3D points that I project on the screen, to get four 2D points. These 8 values are kinda noisy, due to the detection, so I want to filter them to make the movement smoother and more realistic. As a second measurement, I use the device's gyroscope output, which provides three Euler angles (i.e. the device attitude). Those are more precise and at greater frequency (up to 100 Hz) than 2D positions (around 20 Hz).
My first attempt was with a simple low-pass filter, but the lag was important, so I now try to use a Kalman filter, hoping it will be able to smooth the positions with little delay. As seen in a previous question, one key point in a Kalman filter is the relation between the measurements and the internal state variables. Here the measurements are both my 8 2D point coordinates and the 3 Euler angles, but I'm not sure about what I should use as internal state variables and how I should connect the Euler angles to the 2D points. Hence the primary question, is a Kalman filter even suitable for this problem? And if yes, how?