A Novel Visual Tracking Method Based on Moth-Flame Optimization Algorithm

Zhang, Huanlong; Zhang, Xiujiao; Qian, Xiaoliang; Chen, Yibin; Wang, Fang

doi:10.1007/978-3-030-03341-5_24

Cited by 6 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gao et al [18] introduced a powerful approach named bat algorithm (BA) to deal with various global optimization problems and BA had successfully solved many challenging issues in visual tracking. Zhang et al [24] presented a new Moth-flame optimization algorithm (MFO)-based visual tracking method. In this work, the spiral flight of moths and the mechanism that reduces the number of flames gradually were employed to enhance tracking capabilities.…”

Section: Related Workmentioning

confidence: 99%

A new visual tracking approach based on salp swarm algorithm for abrupt motion tracking

2020

KSII TIIS

View full text Add to dashboard Cite

Salp Swarm Algorithm (SSA) is a new nature-inspired swarm optimization algorithm that mimics the swarming behavior of salps navigating and foraging in the oceans. SSA has been proved to enable to avoid local optima and enhance convergence speed benefiting from the adaptive nonlinear mechanism and salp chains. In this paper, visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. A novel SSA-based tracking framework is proposed and the analysis and adjustment of parameters are discussed experimentally. Besides, the qualitative analysis and quantitative analysis are performed to demonstrate the tracking effect of our proposed approach by comparing with ten classical tracking algorithms. Extensive comparative experimental results show that our algorithm has good performance in visual tracking, especially for abrupt motion tracking.

show abstract

Section: Related Workmentioning

confidence: 99%