We consider the problem of building a transitional model of an initially uncalibrated camera network. More specifically, we discuss a Hidden Markov Model (HMM) based strategy in which the model’s state-space is defined in terms of a partition of the physical network coverage. Transitions between any two such states are modeled by the distribution of the underlying Markov Process. Extending previous work in (Cenedese et al., 2010), we show how it is possible to infer the model structure and parameters from coordinate free observations and introduce a novel performance index that is used for model validation. We moreover show the predictive power of this HMM approach in simulated and real settings that comprise Pan-Tilt-Zoom (PTZ) cameras.

An Hidden Markov Model based transitional description of camera networks

CENEDESE, ANGELO;CARLI, RUGGERO
2014

Abstract

We consider the problem of building a transitional model of an initially uncalibrated camera network. More specifically, we discuss a Hidden Markov Model (HMM) based strategy in which the model’s state-space is defined in terms of a partition of the physical network coverage. Transitions between any two such states are modeled by the distribution of the underlying Markov Process. Extending previous work in (Cenedese et al., 2010), we show how it is possible to infer the model structure and parameters from coordinate free observations and introduce a novel performance index that is used for model validation. We moreover show the predictive power of this HMM approach in simulated and real settings that comprise Pan-Tilt-Zoom (PTZ) cameras.
2014
Proceedings of the 19th IFAC World Congress
9783902823625
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2806136
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