Vrednotenje učinkovitosti Kalmanovega filtra pri sledenju ljudi

Kalman filtering (KF) is a standard technique for estimating position and uncertainty of a moving object based on noisy measurements and knowledge of object dynamics. In this paper we apply the Kalman filter algorithm to estimate the motion parameters (position and speed) of a moving Peršon from a video stream. To assess the efficiency of KF tracking various experiments with and without KF were performed. The results showed that modeling of a Peršon motion and measurement noise using KF algorithm can considerably improve the tracking performance in cases of human interactions and occlusions.