Human Detection Using SURF and SIFT Feature Extraction Methods in Different Color Spaces

Local feature matching has become a commonly used method to compare images. For tracking and human detection, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. two different types of image feature algorithms, Scale -Invariant Feature Transform (SIFT) and the more recent Speeded Up Robust Features (SURF), have been used to compare the images. In this paper, we propose the use of a rich set of feature descriptors based on SIFT and SURF in the different color spaces.