Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

Finding trends in motion using the Kalman filter

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 ...