iTrack: Image-based probabilistic tracking of people

Real applications on people tracking are usually based on image heuristics. Real approaches do not use to apply recent prediction-estimation theoretical frameworks. These require the definition of complex dynamical and shape object models before the tracking process. We present a probablistic framework that takes profit of these theories adapting them to real applications. The key idea of this work is to estimate the shape model and dynamical objects parameters using only image data. The flexibility of our algorithm makes it suitable to be used on different real applications. Some experiments have been done in order to test our method in outdoor scenes people tracking.