Open AccessTarget tracking approach via quantum genetic algorithm
Author(s) -
Jin Zefenfen,
Hou Zhiqiang,
Yu Wangsheng,
Wang Xin
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0176
Subject(s) - fitness function , genetic algorithm , computer science , artificial intelligence , feature (linguistics) , pixel , algorithm , tracking (education) , computer vision , position (finance) , pattern recognition (psychology) , machine learning , psychology , pedagogy , philosophy , linguistics , finance , economics
Aiming at an efficient feature match and similarity search in visual tracking, this study proposes a tracking algorithm based on quantum genetic algorithm. Therein, the global optimisation ability of quantum genetic algorithm is utilised. In the framework of quantum genetic algorithm, the positions of pixels are taken as individuals in population, while scale‐invariant feature transform and colour features are taken as target model. Via defining the objective function, individual's fitness values can be measured. Visual tracking is realised when the pixel point with the biggest fitness value is searched and its corresponding position is returned. The experiment results show that the tracking algorithm the authors proposed performs more efficiently when it is compared with the state‐of‐the‐art tracking algorithms.