Open AccessNovel visual tracking approach via ant lion optimiser
Author(s) -
Zhang Huanlong,
Gao Zeng,
Zhang Jie,
Lu Xiankai,
Chen Jian,
Nie Guohao,
Qian Xiaoliang
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5702
Subject(s) - artificial intelligence , computer science , bittorrent tracker , computer vision , eye tracking , tracking (education) , kernel (algebra) , video tracking , visualization , object (grammar) , mathematics , psychology , pedagogy , combinatorics
Ant lion optimiser (ALO) is a new nature‐inspired swarm intelligence optimisation algorithm that mimics the hunting mechanism of antlions in nature. ALO has been proved to have the merits of high exploitation and convergence speed benefiting from adaptive boundary shrinking mechanism and elitism. In this work, visual tracking is expressed as searching for object in whole search space by interaction between antlions and ants. A novel ALO‐based visual tracking framework is proposed and the adaptation and sensitivity of the parameters in ALO are discussed to improve tracking performance. In addition, considering that ALO tracker needs a lot of iteration consumption, kernel correlation filter with deep feature is integrated into the ALO tracking framework (ALOKCF) to improve track efficiency. Extensive experimental results prove that the ALO tracker is very competitive compared to other trackers, especially for abrupt motion tracking. At the same time, two visual tracking benchmarks are used to verify ALOKCF tracker achieves state‐of‐the‐art performance.