Object detection based on template matching by using enhanced global-best ABC

In recent years, Artificial Bee Colony (ABC) optimization algorithm has captured much attention of researchers from various fields. Moreover, various comparative studies clearly reports robust convergence of ABC algorithm than other bio-inspired optimization algorithms. Nevertheless, like other optimization algorithms, ABC suffers from slower convergence and tendency towards local optima trappings. Various amendments have been proposed to avert the flaws of ABC algorithm. Hence, this research work proposes an efficient variant of ABC algorithm. The proposed variant [1] capitalizes on the global-best food-source. In this work, we aim to apply the Enhanced Global-Best 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.