Face feature tracking algorithm of aerobics athletes based on Kalman filter and mean shift
by Shu Yang
International Journal of Biometrics (IJBM), Vol. 14, No. 3/4, 2022

Abstract: In order to solve the problems of low accuracy and long time-consuming in face image tracking of aerobics athletes in traditional methods, a face feature tracking algorithm based on Kalman filter and mean shift is proposed. Three-frame difference method is used to extract the colour features of aerobics athletes' face images, measure the geometric feature similarity of aerobics athletes' face images, calculate the grey value of local images of aerobics athletes' face features, and match corner features by NCC matching algorithm. The Kalman filter method is introduced to denoise the different pixels of the feature image, and the mean shift of the aerobics athletes' face features is obtained by means of the mean shift algorithm to realise the tracking of the aerobics athletes' face features. The experimental results show that the tracking accuracy of the proposed method is up to 97%, and the shortest tracking time is about 1.5 s.

Online publication date: Fri, 05-Aug-2022

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