Object detection based on template matching through use of Best-So-Far ABC

Template matching is a technique in computer vision used for finding a sub-image of a target image which matches a template image.

The template matching technique requires extensive computational cost since the matching process involves moving the template image to all possible positions in a larger target image and computing a numerical index that indicates how well the template matches the image in that position. Therefore, this problem can be considered as an optimization problem. The algorithms based on swarm intelligence approach have been considered as a way to alleviate the drawback of the long processing time in this problem.

This study employs the best-so-far ABC (Banharnsakun, 2011) to improve the solution quality in detecting the target objects and to optimize the time used to reach the solution.

The software solution was developed in C/C++. Banharsakun, who proposed the Best-so-far ABC, took the major part in integrating the best-so-far ABC approach with the matching.

The detected object results from the best-so-far ABC with RGB histogram:

Publications:
1. Banharnsakun, A., Tanathong, S., 2014. Object detection based on template matching through use of Best-So-Far ABC. Computational Intelligence and Neuroscience, vol. 2014. [LINK]
2. Tanathong, S., Banharnsakun, A., 2014. Multiple Object Tracking Based on a Hierarchical Clustering of Features Approach. In Proceedings of ACIIDS, Bangkok, Thailand. [LINK]