Markerless identification and tracking for scalable image database

In this paper we present a novel approach for object identification and tracking in large image datasets. Objects of interest are represented by feature points and descriptors extracted and compared to a set of reference data. An optimized matching paradigm is designed to deal with scalable image databases while keeping a good recognition rate in real-life environment conditions. Experiments are conducted to evaluate the effectiveness of the method and the obtained results demonstrate a true interest of the proposed approach.