The Kalman filter is an algorithm developed mainly (but not exclusively) by Rudolf Kalman in the late 1950s. It has found practical applications in many fields, particularly navigation systems for all sorts of vehicles from nuclear submarines to aircraft.
The Kalman filter operates recursively on a stream of noisy input data to produce a statistically optimal estimate of the underlying system state. In the context of computer vision, the Kalman filter can smoothen the estimate of a tracked object's position.
Let's consider a simple example. Think of a small red ball on a table and imagine you have a camera pointing at the scene. You identify the ball as the subject to be tracked, and flick it with your fingers. The ball will start rolling on the table in accordance with the laws of motion.
If the ball is rolling at a...
