New matching method for human body tracking

In this paper we present a new method for interest points matching to realize human body tracking in video sequences. The developed algorithm combines direct and indirect similarity measures evaluated when applying luminosity variation and motion blur noises. This new approach considers different matching constraints such as: cross-matching, uniqueness constraint and interest point´s appearances and disappearances between consecutive images. The algorithm was evaluated on two different datasets and leads to high values of Good Tracking Rate.