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      A Bayesian computer vision system for modeling human interactions

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          Pfinder: real-time tracking of the human body

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            Modeling and prediction of human behavior.

            We propose that many human behaviors can be accurately described as a set of dynamic modes (e.g., Kalman filters) sequenced together by a Markov chain. We then use these dynamic Markov models to recognize human behaviors from sensory data and to predict human behaviors over a few seconds time. To test the power of this modeling approach, we report an experiment in which we were able to achieve 95% accuracy at predicting automobile drivers' subsequent actions from their initial preparatory movements.
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              A guide to the literature on learning probabilistic networks from data

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                Author and article information

                Journal
                IEEE Transactions on Pattern Analysis and Machine Intelligence
                IEEE Trans. Pattern Anal. Machine Intell.
                Institute of Electrical and Electronics Engineers (IEEE)
                01628828
                Aug. 2000
                : 22
                : 8
                : 831-843
                Article
                10.1109/34.868684
                e3b79226-cfd7-4398-9454-9d490d9f107d
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