Probabilistic image-based tracking: improving particle filtering
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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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 probabilistic 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 object 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.