title: Object detection based on template matching through use of best-so-far ABC.
abstract: Best-so-far ABC is a modified version of the artificial bee colony (ABC) algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI) algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution.
authors.affiliation: Laboratory for Computational Intelligence, Faculty of Engineering at Sriracha, Kasetsart University Sriracha Campus, Chonburi 20230, Thailand.
authors.forename: Anan
authors.identifier: 0000-0003-2004-4070
authors.initials: A
authors.lastname: Banharnsakun
authors.affiliation: Laboratory for Sensor and Modeling, Department of Geoinformatics, University of Seoul, Seoul 130-743, Republic of Korea.
authors.forename: Supannee
authors.identifier: 0000-0002-8536-5011
authors.initials: S
authors.lastname: Tanathong
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001516:Bees; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*