A synergy of the adaptive whale optimization algorithm and differential evolution for abrupt motion tracking

Abstract

Aiming at the problem that conventional trackers are difficult to adapt to abrupt motion, a tracking algorithm based on a synergy of the adaptive whale optimization algorithm and differential evolution (AWOA-DE) is proposed in this paper. Firstly, the non-linear dynamic feedback strategy based on the one-fifth principle is employed to replace the linear mechanism, which is the variation of exploring step in the basic whale optimization algorithm (WOA), and an adaptive WOA (AWOA) is generated to improve the global optimization ability of the WOA. Secondly, AWOA adopts a random sampling strategy to generate new samples, which could ensure the diversity of solutions, but may lead to instability. By mixing AWOA and DE algorithms, the population is divided into two groups, which are updated by AWOA and DE algorithms respectively, to ensure the diversity of the populations and improve the optimization accuracy of the algorithm. Finally, a visual tracker based on AWOA-DE is proposed, which can deal with the problem of abrupt motion in tracking. In addition, the effectiveness of the hybrid optimization algorithm is evaluated using 23 benchmark functions. In the experiment of visual tracking, extensive experimental results show that the AWOA-DE tracker (AWOADET) has much stronger competitiveness compared to the WOA tracker (WOAT) and the other 10 state-of-the-art trackers, especially for abrupt motion tracking.