Object Detection Using Feature-based Template Matching

Pattern matching, also known as template matching, is a computationally intensive problem aimed at localizing the instances of a given template within a query image. In this work we present a fast technique for template matching, able to use histogram-based similarity measures on complex descriptors. In particular we will focus on Color Histograms (CH), Histograms of Oriented Gradients (HOG), and Bag of visual Words histograms (BOW). The image is compared with the template via histogram-matching exploiting integral histograms. In order to introduce spatial information, template and candidates are divided into sub-regions, and multiple descriptor sizes are computed. The proposed solution is compared with the Full-Search-equivalent Incremental Dissimilarity Approximations, a state of the art approach, in terms of both accuracy and execution time on different standard datasets.