This paper presents a new method of detecting and predicting motion tracking failures with applications in human motion and gait analysis. We define a tracking failure as an event and describe its temporal characteristics using a hidden Markov model (HMM). This stochastic model is trained using previous examples of tracking failures. We derive vector observations for the HMM using the noise covariance matrices characterizing a tracked, 3D structural model of the human body. We show a causal relationship between the conditional output probability of the HMM, as transformed using a logarithmic mapping function, and impending tracking failures. Results are illustrated on several multi-view sequences of complex human motion.


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    Title :

    Markov-based failure prediction for human motion analysis


    Contributors:
    Dockstader, (author) / Imennov, (author) / Tekalp, (author)


    Publication date :

    2003-01-01


    Size :

    573997 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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