Title: Face feature tracking algorithm for long-distance runners based on multi-region fusion

Authors: Wan Li

Addresses: Chongqing Vocational and Technical University of Mechatronics, Bishan, Chongqing 402760, China

Abstract: In order to overcome the problems of high error rate and poor tracking effect of traditional algorithms, a multi-region fusion-based feature tracking algorithm for long-distance runners was proposed in this paper. Firstly, the multi-region template voting strategy is adopted to classify and obtain face features by dividing face feature similarity threshold in different regions through regional feature similarity classification. Then, the mean shift tracking algorithm was used to complete the target object modelling, and the pap coefficient was used as the evaluation standard of model similarity measurement, and the face features were tracked through iterative operation. Experimental results show that the recognition accuracy of this algorithm is higher than 92% in different situations, and the tracking error of the centre position is always below 20 pixels in different angles and complex environments, which fully proves the effectiveness of this algorithm.

Keywords: multi-region fusion; long-distance runner; feature classification; face feature tracking; mean shift tracking algorithm.

DOI: 10.1504/IJBM.2022.124673

International Journal of Biometrics, 2022 Vol.14 No.3/4, pp.303 - 317

Received: 27 Aug 2020
Accepted: 04 Nov 2020

Published online: 05 Aug 2022 *

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