Title: Face feature tracking algorithm of aerobics athletes based on Kalman filter and mean shift

Authors: Shu Yang

Addresses: Physical Culture Institute, Hunan International Economics University, Changsha, 401205, China

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.

Keywords: multi region fusion; aerobics athletes; image resolution; background modelling; feature tracking.

DOI: 10.1504/IJBM.2022.124679

International Journal of Biometrics, 2022 Vol.14 No.3/4, pp.394 - 407

Received: 29 Sep 2020
Accepted: 15 Dec 2020

Published online: 05 Aug 2022 *

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