Video target tracking algorithms based on Kalman filter require prior information

such as process noise and observation noise variance. However

we cannot determine the exact values of both noise variance in practical applications. Moreover

noise variance occasionally changes dynamically because of target randomness and background video scene complexity. If noise variance is inaccurate

tracking accuracy is degraded or tracking failure is brought out. In view of these problems

a new solution is proposed in this paper. In combination with the forgetting factor recursive least squares (EFRLS) method

a new algorithm that is applied to video target tracking without use of noise variance is presented in this article. First

mean shift is used to obtain a preliminary estimate of the target position. Then

the EFRLS method is used to estimate the position in the next frame. Experimental results show that the proposed algorithm is significantly better than traditional mean shift algorithm and is equivalent to Kalman tracking algorithm combined with mean shift. In addition

if severe occlusions exist in between targets

this algorithm is better than Kalman tracking algorithm combined with mean shift. The proposed algorithm also has good tracking performance. Setting parameters of noises is not necessary. Accurate tracking results can be achieved when serious occlusions exist or re-emerging occurs after occlusions. The robustness of the new algorithm is then enhanced

which can be used for some engineering applications.